# ppiTrim: constructing non-redundant and up-to-date interactomes

Aleksandar Stojmirović and Yi-Kuo Yu∗
National Center for Biotechnology Information,
National Library of Medicine, National Institutes of Health,
Bethesda, MD 20894, United States
<EMAIL_ADDRESS>and<EMAIL_ADDRESS>
###### Abstract

Robust advances in interactome analysis demand comprehensive, non-redundant
and consistently annotated datasets. By non-redundant, we mean that the
accounting of evidence for every interaction should be faithful: each
independent experimental support is counted exactly once, no more, no less.
While many interactions are shared among public repositories, none of them
contains the complete known interactome for any model organism. In addition,
the annotations of the same experimental result by different repositories
often disagree. This brings up the issue of which annotation to keep while
consolidating evidences that are the same. The iRefIndex database, including
interactions from most popular repositories with a standardized protein
nomenclature, represents a significant advance in all aspects, especially in
comprehensiveness. However, iRefIndex aims to maintain all
information/annotation from original sources and requires users to perform
additional processing to fully achieve the aforementioned goals. Another issue
has to do with protein complexes. Some databases represent experimentally
observed complexes as interactions with more than two participants, while
others expand them into binary interactions using spoke or matrix model. To
avoid untested interaction information buildup, it is preferable to replace
the expanded protein complexes, either from spoke or matrix models, with a
flat list of complex members.

To address these issues and to achieve our goals, we have developed ppiTrim, a
script that processes iRefIndex to produce non-redundant, consistently
annotated datasets of physical interactions. Our script proceeds in three
stages: mapping all interactants to gene identifiers and removing all
undesired raw interactions, deflating potentially expanded complexes, and
reconciling for each interaction the annotation labels among different source
databases. As an illustration, we have processed the three largest organismal
datasets: yeast, human and fruitfly. While ppiTrim can resolve most apparent
conflicts between different labelings, we also discovered some unresolvable
disagreements mostly resulting from different annotation policies among
repositories.
URL: www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads/ppiTrim.html

## Introduction

The current decade has witnessed a significant amount of effort towards
discovering the networks of protein-protein interactions (interactomes) in a
number of model organisms. These efforts resulted in hundreds of thousands of
individual interactions between pairs of proteins being reported (1).
Repositories such as the BioGRID (2), IntAct (3), MINT (4), DIP (5), BIND (6,
7) and HPRD (8) have been established to store and distribute sets of
interactions collected from high-throughput scans as well as from curation of
individual publications. Depending on its goals, each interaction database,
maintained by a different team of curators located around the world includes
and annotates interactions differently. Consequently, while many interactions
of specific interactomes are shared among databases (1, 9), no one contains
the complete known interactome for any model organism. Constructing a full-
coverage protein-protein interaction network therefore requires retrieving and
combining entries from many databases.

This task is facilitated by several initiatives developed by the proteomics
community over the years. The IMEx consortium (10) was formed to facilitate
interchange of information between different primary databases by using a
standardized format. The Proteomics Standards Initiative Molecular Interaction
(PSI-MI) format (11) allows a standard way to represent protein interaction
information. One of its salient features is the controlled vocabulary of terms
that can be used to describe various facets of a protein-protein interaction
including source database, interaction detection method, cellular and
experimental roles of interacting proteins and others. The PSI-MI vocabulary
is organized as an ontology, a directed acyclic graph (DAG), where nodes
correspond to terms and links to relations between terms. This enables the
terms to be related in an efficient and algorithm-friendly manner.

Consistently annotated datasets are useful for development and assessment of
interaction prediction tools (12, 13, 14, 15). Furthermore, such datasets also
form the basis of interaction networks, for which numerous analysis tools have
been developed (16, 17). Depending on biological aims of a tool, different
entities (nodes) and potentially weighted interactions (edges) may be
preferred. The chance of conflicting predictions from different tools can be
reduced by starting from a consistently annotated dataset that faithfully
represents all available evidences. Such dataset ought to be comprehensive but
also non-redundant: the same experimental evidence for an interaction should
appear once and only once. To maintain a coherent development of biological
understanding, it is indispensable to keep the reference datasets up-to-date.

We examined several primary interaction databases with the aim of constructing
non-redundant (in terms of evidence), consistently annotated and up-to-date
reference datasets of physical interactions for several model organisms.
Unfortunately, the common standard format used by most primary databases still
does not allow direct compilation of full non-redundant interactomes. This
mainly results from the fact that different primary databases may use
different identifiers for interacting proteins and different conventions for
representing and annotating each interaction. Combining interaction data from
BIND (6, 7) (in two versions called ‘BIND’ and ‘BIND_Translation’), BioGRID
(2), CORUM (18), DIP (5), HPRD (8), IntAct (3), MINT (4), MPact (19), MPPI
(20) and OPHID (21), the iRefIndex (22) database represents a significant
advance towards a complete and consistent set of all publicly available
protein interactions. Apart from being comprehensive and relatively up-to-
date, the main contribution of iRefIndex is in addressing the problem of
protein identifiers by mapping the sequence of every interactant into a unique
identifier that can be used to compare interactants from different source
databases. In a further ‘canonicalization’ procedure (23), different isoforms
of the same protein are mapped to the same canonical identifier. By adhering
to the PSI-MI vocabulary and file format, iRefIndex provides largely
standardized annotations for interactants and interactions. Construction of
iRefIndex led to the development of iRefWeb, a web interface for interactive
access to iRefIndex data (23). iRefWeb allows an easy visualization of
evidence for interactions associated with user-selected proteins or
publications. Recently, the authors of iRefIndex and iRefWeb published a
detailed analysis of agreement between curated interactions within iRefIndex
that are shared between major databases (24).

However, aiming to maintain all information from original sources, iRefIndex
requires users to perform additional processing to fully achieve the
aforementioned goals. In particular, iRefIndex considers redundancy in terms
of (unordered) pairs of interactants rather than in terms of experimental
evidence associated with an interaction. Consequently, there will be features
one desires to have that may not fit well within the scope of iRefIndex. For
example, one may wish to treat interactions arising from enzymatic reactions
as directed and to be able to selectively include/exclude certain types of
reactions such as acetylation. In many cases, the information about post-
translational modifications is available directly from source databases, but
is not integrated into iRefIndex. Another issue that propagates into iRefIndex
from source databases has to do with protein complexes. Some databases
represent experimentally observed complexes as interactions with more than two
participants, while others expand them into binary interactions using spoke or
matrix model (1). Turinsky _et al._ (24) recently observed that this different
representation of complexes is responsible for a significant number of
disagreements between major databases curating the same publication. From our
earlier work (25), we found that such expanded complexes may lead to nodes
with very high degree and often introduce undesirable shortcuts in networks.
To fairly treat the information provided by protein complexes without
exaggeration, it is preferable to replace the expanded interactions, either
from spoke or matrix models, with a flat list of complex members.
Additionally, we discovered that the mapping of each protein to a canonical
group by iRefIndex would sometimes place protein sequences clearly originating
from the same gene (for example differing in one or two amino acids) into
different canonical groups.

To achieve the goal of constructing non-redundant, consistently annotated and
up-to-date reference datasets, we developed a script, called ppiTrim, that
processes iRefIndex and produces a consolidated dataset of physical protein-
protein interactions within a single organism.

## Materials and Methods

Our script, called ppiTrim, is written in the Python programming language. It
takes as input a dataset in iRefIndex PSI-MI TAB 2.6 format, with 54 TAB-
delimited columns (36 standard and 18 added by iRefIndex). After three major
processing steps, it outputs a consolidated dataset, in PSI-MI TAB 2.6 format,
containing only the 36 standard columns (Supplementary Table 1). The three
processing steps are: (i) mapping all interactants to NCBI Gene IDs and
removing all undesired raw interactions; (ii) deflating potentially expanded
complexes; and (iii) collecting all raw interactions, originated from a single
publication, that have the same interactants and compatible experimental
detection method annotations into one consolidated interaction. At each step,
ppiTrim downloads the files it requires from the public repositories and
writes its intermediate results as temporary files.

### Phase I: initial filtering and mapping interactants

In Phase I, ppiTrim takes the original iRefIndex dataset and classifies each
raw interaction (either a binary interaction corresponding to a single line in
the input file or a complex supported by several lines) into one of four
distinct categories: removed (not examined further), biochemical reaction,
complex or potentially part of a complex, and other (direct binary binding
interaction). It removes interactions marked as genetic, originating from
publications specified through a command line parameter or having interactants
from organisms other than the main species of the input dataset (the allowed
species can be explicitly provided or any interaction with interactants having
different Taxonomy IDs is removed). Additionally, ppiTrim removes all
interactions from OPHID and the ‘original’ BIND. The former is removed because
it contains either computationally predicted interactions or interactions
verified from the literature using text mining (i.e. without human curation).
The latter is removed because it processes the same original dataset as
BIND_Translation (7).

As a first step, the script seeks to map each interactant to an NCBI Entrez
Gene (26) identifier. For most interactants, it uses the mapping already
provided by iRefIndex. In the cases where iRefIndex provides only a Uniprot
(27) knowledge base accession, the script attempts to obtain a Gene ID  in
three different ways. First, it searches the iRefIndex mappings.txt file
(found compressed in ftp.no.embnet.org/irefindex/data/current/Mappingfiles/
for any additional mappings. This part is optional because the mappings.txt
file is very large even compressed and it would not be feasible to perform
automatic download each time ppiTrim is run. Second, for all unmapped Uniprot
IDs, it retrieves the corresponding full Uniprot records using the dbfetch
tool from EBI (www.ebi.ac.uk/Tools/dbfetch). If a direct mapping to Gene ID is
present within the record as a part of DR field, it is used. Otherwise, the
canonical gene name (field GN) is used to query the NCBI Entrez Gene database
for a matching Gene record using an Eutils interface. If a single unambiguous
match is found, the record’s Gene ID is used for the interactant. No mapping
is performed if multiple matches are obtained. Every mapped Gene ID is checked
against the list of obsolete Gene IDs, which are no longer considered to have
a protein product existing in vivo. The interactants that cannot be mapped to
valid (non-obsolete) Gene IDs are removed along with all raw interactions they
participate in.

After assigning Gene IDs, the script considers the PSI-MI ontology terms
associated with interaction detection method, interaction type and
interactants’ biological roles. Using the full PSI-MI ontology file in Open
Biomedical Ontology (OBO) format (28), it replaces any non-standard terms in
these fields (labeled MI:0000) with the corresponding valid PSI-MI ontology
terms. The terms marked as obsolete in the PSI-MI OBO file are exchanged for
their recommended replacements (Supplementary Table 2). The single exception
are the interaction detection method terms for HPRD ‘in vitro’ (MI:0492,
translated from MI:0045 label in iRefIndex) and ‘in vivo’ (MI:0493)
interactions, which are kept throughout the entire processing.

Source interactions annotated with a descendant of the term MI:0415 (enzymatic
study) as their detection method or with a descendant of the term MI:0414
(enzymatic reaction) as their interaction type are classified as candidate
biochemical reactions. This category also includes any interactions (including
those with more than two interactants) where one of interactants has a
biological role of MI:0501 (enzyme) or MI:0502 (enzyme target). In the recent
months, the BioGRID database has started to provide additional information
about the post-translational modifications associated with the ‘biochemical
activity’ interactions, such as phosphorylation, ubiquitination etc. This
information is available from the BioGRID datasets in the new TAB2 format but
is not yet reflected in the PSI-MI terms for interaction type provided in the
PSI-MI 2.5 format or in iRefIndex. Since the post-translational modifications
annotated by the BioGRID can be directly matched to standard PSI-MI terms
(Supplementary Table 3), the script downloads the most recent BioGRID dataset
in TAB2 format, extracts this information and assigns appropriate PSI-MI terms
for interaction type to the candidate biochemical reactions from iRefIndex
that originate from the BioGRID.

Any source interaction not classified as candidate biochemical reaction is
considered for assignment to the candidate complex categories. This category
includes all true complexes (having edge type ‘C’ in iRefIndex), interactions
having a descendant of MI:0004 (affinity chromatography) as the detection
method term or MI:0403 (colocalization) as the interaction type, as well as
the interactions corresponding to the BioGRID’s ‘Co-purification’ category.
Interactions with interaction type MI:0407 (direct interaction) are never
considered candidates for complexes. All source interactions not falling into
candidate biochemical reaction or candidate complex categories are considered
ordinary binary physical interactions.

### Phase II: deflating spoke-expanded complexes

The Phase II script attempts to detect spoke-expanded complexes from
‘candidate complex’ interactions and deflate them into interactions with
multiple interactants. First, all candidate interactions are grouped according
to their publication (Pubmed ID), source database, detection method and
interaction type. Each group of source interactions is turned into a graph and
considered separately for consolidation into one or more complexes. When a
portion of a group of interactions is deflated, we replace these source
interactions by a complex containing all their participants. Each collapsed
complex is represented using bipartite representation in the output MITAB file
(the same as the original complexes from iRefIndex, but using newly generated
complex IDs) and the references to the original source interactions are
preserved (Supplementary Table 1). Two procedures are used for consolidation:
pattern detection and template matching (Fig. 1). The deflation algorithm for
each new complex is indicated in the output file through its edge type (Table
1).

Figure 1: ppiTrim uses two procedures for complex deflation: pattern detection
(top) and template matching (bottom). As an example, assume that a graph
ABCDEFG, shown on the left, could be constructed from complex candidate
interactions annotated by the BioGRID from a single publication. The arrows
indicate bait to prey relationships, with the interaction A–D being repeated
twice, once with A and once with D as a bait. Pattern detection algorithm
(top) would recognize A and D as hubs of potentially spoke-expanded complexes
and thus replace all pairwise interactions on the left with complexes ABCDEF
and ACDEFG. Suppose that the complex ACDEF was reported from the same
publication by a different database. Then, template matching procedure
(bottom) would generate the complex ACDEF (with all other annotation, such as
experimental detection method, retained from the original interactions) and
remove all original interactions except D–G and A–B. After performing both
procedures, ppiTrim consolidates the results so that the overall result would
be replacing the original interactions by complexes ACDEF, ABCDEF and ACDEFG
with edge type codes ‘R’, ‘A’ and ‘A’, respectively. The interactions A–B and
D–G would not be retained since they are contained within the deflated
complexes ABCDEF and ACDEFG.

Pattern detection procedure is used only for the interactions from the
BioGRID. Unlike the interactions from the DIP, those interactions are
inherently directed since one protein is always labeled as bait and other as
prey (in many cases this labeling is unrelated to the actual experimental
roles of the proteins). The pattern indicating a possible spoke-expanded
complex consists of a single bait being linked to many preys. Since all
interactions in the BioGRID’s ’Co-purification’ and ’Co-fractionation’
categories arise from complexes that are spoke-expanded using an arbitrary
protein as a bait (BioGRID Administration Team, private communication), a bait
linked to two or more preys can in that case always be considered an expanded
complex and deflated. Such deflated complexes are assigned the edge type code
‘G’. The remainder of the complex candidate interactions from the BioGRID were
obtained by affinity chromatography and are, in most cases, also derived from
complexes. Here we adopted a heuristic that a bait linked to at least three
preys can be considered a complex. Clearly, some experiments involve a single
bait being used with many independent preys, in which case this procedure
would generate a false complex. Therefore, complexes generated in this way are
assigned a different edge type code (‘A’) and the user is able to specify
specific publications to be excluded from consideration as well as the maximal
size of the complex.

Table 1: Edge type codes used by ppiTrim  Code |  Description
---|---
X |  undirected binary interaction (physical binding)
D |  directed binary interaction (biochemical reaction)
B |  biochemical reaction without indication of directionality
C |  original complex (from iRefIndex)
G |  spoke-expanded complex; deflated by pattern matching from BioGRID’s ’Co-purification’ and ’Co-fractionation’ categories (reliable)
R |  potential spoke-expanded complex; deflated by template matching of a ‘C’-complex
A |  potential spoke-expanded complex (BioGRID only); deflated by pattern detection
N |  potential spoke-expanded complex; deflated by template matching of a ‘G’- or ‘A’-complex

The second procedure is based on matching each group of candidate interactions
to the complexes indicated by other databases (templates), mostly from IntAct,
MINT, DIP and BIND. In this case, the script checks for each protein in the
group whether it, together with all its neighbors, is a superset of a template
complex. If so, all the candidate interactions between the proteins within the
complex are deflated. The neighborhood graph is undirected for all source
databases except the BioGRID. The new complexes generated in this way are
given the code ‘R’. The scripts also attempts to use complexes generated from
the BioGRID’s interactions through a pattern detection procedure as templates,
in which case the newly generated complexes have the code ‘N’. Any source
interactions that cannot be deflated into complexes are retained for Phase
III.

### Phase III: Normalizing interaction type annotation

#### Overview

The goal of the final phase of ppiTrim is to consolidate all evidence for an
interaction, obtained from a single experiment, into one _consolidated
interaction_ record. Every source publication contains descriptions of one or
more experiments that result in reported interactions. Unfortunately, distinct
experiments within each publication are not annotated in all source databases,
with the exception of the interactions from IntAct and MINT that appear to
distinguish experiments using a numbered suffix to the author’s name in the
‘Author’ field. It is therefore necessary to rely on the experimental
detection method terms to determine whether source records from different
databases, with the same interactants and source publication, represent the
evidence for the same interaction. Ideally, all such records with the same
detection method can be collapsed into one consolidated interaction, although
this may undercount multiple evidences from the same publication obtained by
distinct experiments. However, different databases have different annotation
policies and do not necessarily use the same PSI-MI term to annotate a given
experimental method. To resolve detection method term disagreements, we use
the PSI-MI ontology structure (Fig. 2). Two compatible terms assigned by
different source databases are considered to represent the same experimental
method within a publication. These annotated records are thus consolidated.

The Phase III algorithm proceeds as follows. All source interactions and
complexes (original as well as deflated in Phase II) are divided into
‘clusters’. Interactions that share the same interactants and the source
publication are placed into the same cluster. The order of interactants is
significant only for biochemical reactions, which are treated as directed
interactions (only when direction can be ascertained). Each cluster is
processed independently and divided into subclusters based on compatibility of
the PSI-MI terms for interaction detection method. Interactions from each
subcluster are collected into a single consolidated interaction, which is
output to the final dataset. The consolidated record preserves references to
all original interactions. Each consolidated interaction is assigned a single
PSI-MI term for interaction detection method that most specifically describes
the entire collection of annotation terms within the subcluster. For easier
reference, each consolidated interaction is given a unique ppiTrim ID, which
is similar to RIGID from iRefIndex. This is a SHA1 hash of a dot-separated
concatenation of its interactants (Gene IDs), publication(s), detection
method, interaction type and edge type. Every complex uses its ppiTrim ID as
its primary ID.

#### Reconciling annotation

The DAG structure of an ontology naturally induces a partial order between the
terms: for two terms $u$ and $v$, we say that $u$ refines $v$ ($u$ is smaller
$v$, $u$ precedes $v$) if there exists a directed path in the DAG from $u$ to
$v$. Two PSI-MI terms can be considered compatible if they are comparable,
that is, one refines the other. Every nonempty collection of terms $U$ can be
uniquely split into disjoint sets $U_{i}$, such that every $U_{i}$ has a
single maximal element (an element comparable to and not smaller than any
other member) and contains all members of $U$ comparable to its maximal
element. Every subcollection $U_{i}$ is then consistent because there exists
at least one term within it that can describe all its members, while any two
members from different subcollections are incomparable. The _finest consistent
term_ of a subcollection $U_{i}$ is the smallest member of $U_{i}$ that is
comparable to all its members (it can also be defined as the smallest member
of the intersection of the transitive closures of all the members of
$U_{i}$.). If $U_{i}$ is a total order, where all members are pairwise
comparable, the finest consistent term is the minimal term. On the other hand,
the minimal term need not exist (Fig. 2), so that the finest consistent term
is higher in the hierarchy and represents the most specific annotation that
can be assigned to $U_{i}$ as a whole.

To produce consolidated interactions from a single cluster, each of its
members (interactions) is identified with its PSI-MI term for information
detection method. For every cluster member, the set of all other members with
compatible annotations (‘compatible set’) is computed. As a special case, the
following detection method tags are treated as smaller than any other:
‘unspecified method’ (MI:0686), ‘in vivo’ and ‘in vitro’ (The latter two are
from HPRD only). In this way, non-specific annotations are considered as
compatible with all other, more specific evidences. Compatible sets are
further grouped according to their maximal elements. Within each group, the
union of the compatible sets produces a subcluster. The finest consistent term
for each subcluster is found by considering all PSI-MI terms on the paths from
the subcluster members to its maximum – the search is not restricted to those
terms that are within the subcluster (Fig. 2).

#### Conflicts

We consider two subclusters of the same cluster to be in an unresolvable
conflict if there is no source database shared between them. This definition
takes into account that a source database may report an interaction several
times for the same publication, using the same or different interaction
detection method. If two databases annotate the same interaction using
incompatible terms, this is most likely due to an error or specific
disagreement about the appropriate label, rather than that each database is
reporting a different experiment from the same publication. Unresolvably
conflicting interaction records, after consolidation, point to each other
using ppiTrim ID in the ‘Confidence’ field.

ppiTrim also collects statistics about resolvable conflicts in its temporary
output files. A resolvable conflict is the case where source interactions
within a single subcluster have compatible but different experimental
detection method labels.

Figure 2: The picture shows a part of the PSI-MI ontology graph for
interaction detection method associated with a hypothetical cluster of source
interactions involving the same interactants from the same publication. The
terms colored blue are associated with the source interactions within the
cluster, while those marked yellow and green are present in the ontology but
do not label any source interaction from the cluster. The entire cluster as
shown is consistent, with the term MI:0401 as the maximal element. Its finest
consistent term is MI:0004 (colored green) since the cluster members smaller
than it are not comparable between themselves. Removing the source
interactions labeled by MI:0401 from the cluster would result in three
distinct subclusters. If two subclusters contain no interaction from the same
source database, they would be reported as conflicts.

### Evaluation of the script

To test ppiTrim, we applied it to the yeast (S. cerevisiae), human (H.
sapiens) and fruitfly (D. melanogaster) datasets from iRefIndex release
8.0-beta, dated Jan 19th 2011. The script was run on June 13th 2011 and used
the then-current versions of Uniprot and NCBI Gene databases. We restricted
protein interactors to allowed NCBI Taxonomy IDs: 4932 and 559292 for yeast,
9606 for human, and 7227 for fruitfly datasets. When processing the yeast
dataset, we accounted for two special cases. First, we specifically removed
the genetic interactions reported by Tong _et al._ (29) because they were not
labeled as genetic for all source databases. Second, we excluded the dataset
by Collins _et al._ (30) from Phase II and retained all its interactions as
binary undirected. This dataset is present only in the BioGRID and can be
considered computationally derived and partially redundant. Collins _et al._
(30) reprocessed the data from Gavin _et al._ (31) and Krogan _et al._ (32) to
obtain an improved set of pairwise interactions. Collins _et al._ (30) used
hierarchical clustering to recover protein complexes, but these are not
present in the BioGRID. In spite of its redundancy, we decided not to entirely
remove this dataset but also not to attempt to deflate its potential complexes
because bait/prey assignments may not be meaningful in this case.

## Results and Discussion

The results of applying ppiTrim to process iRefIndex 8.0 are shown in Tables 2
– 5. The statistics of ID mapping (Tables 2 and 3) show that a considerable
number of interactants could be additionally mapped to Gene ID in human and
fruitfly datasets, thus enabling us to take into consideration a few thousand
of raw interactions that would otherwise be filtered. This is also evident in
terms of iRefIndex RIGIDs (Supplementary Table 4), which associate all raw
interactions with interactants with same sequences to a single record. For
yeast, the number of interactions gained by mapping to Gene IDs is small
because most of mapped IDs were not valid.

Table 2: Processing source interactions  Species | Initial | Removed | Without Gene ID | Retained | With Mapped Gene ID
---|---|---|---|---|---
S. cerevisiae | 400449 | 173815 | 3608 | 223026 | 880
H. sapiens | 382094 | 148724 | 2738 | 230632 | 16187
D. melanogaster | 154770 | 32477 | 9476 | 112817 | 3427
Statistics of initial processing of raw interactions from iRefIndex. Shown are the initial number, total number removed due to filtering criteria, number removed due to missing Gene ID, total number of retained and the number retained containing at least one interactant with mapped Gene ID. Table 3: Mapping CROGID identifiers from iRefIndex into Gene IDs  Species | Initial CROGIDs | Aditional Mapped | Final
---|---|---|---
total |  mapped |  orphans |  total |  valid |  CROGIDs |  Gene IDs
S. cerevisiae |  6159 |  5552 |  607 |  433 |  47 |  5599 |  5618
H. sapiens |  14047 |  11432 |  2615 |  1261 |  1261 |  12693 |  11786
D. melanogaster |  9379 |  7810 |  1569 |  566 |  566 |  8346 |  7846
Statistics of mapping CROGIDs into Gene IDs. Columns 2-4 show the total number
of CROGIDs considered, the number that could be directly mapped to GeneIDs and
the number of ‘orphans’ that are not associated with a Gene ID in the
iRefIndex file. Columns 5 and 6 show the numbers of CROGIDs additionally
mapped to GeneIDs, while the last two columns show the final number of CROGIDs
accepted and the corresponding number of Gene IDs. It is possible for a CROGID
to map to multiple Gene IDs (if multiple genes encode the same protein
sequence) as well as for multiple CROGIDs to map to a single GeneID (if our
additional mapping links them to the same gene).

We chose to standardize proteins using NCBI Gene identifiers rather than the
iRefIndex-provided canonical IDs (CROGIDs) for several reasons. NCBI Gene
records not only associate each gene with a set of reference sequences, but
also include a wealth of additional data (e.g. list of synonyms) and links to
other databases such as Gene Ontology (33) that are important when using the
interaction dataset in practice. In addition, Gene records are regularly
updated and their status evaluated based on new evidence. Thus, a gene record
may be split into several new records or marked as obsolete if it corresponds
to an ORF that is known not to produce a protein. For network analysis
applications, it is desirable that only the proteins actually expressed in the
cell are represented in the network and hence the gene status provided by NCBI
Gene is a valuable filtering criterion. Our results in yeast (Table 3) support
this premise: most CROGIDs without Gene ID are associated with sequences
derived from ORFs that were subsequently declassified as genes. However,
CROGIDs do have one advantage over NCBI Gene IDs in that they are protein-
based and hence identical protein products of several genes (like histones)
are clustered together.

There are several reasons that our algorithm was able to introduce many
additional associations of CROGIDs to Gene IDs. First, iRefIndex only provides
mappings to Gene IDs for interactors that have a sequence that exactly matches
a sequence in an NCBI RefSeq record (Ian Donaldson, private communication). By
a case-by-case examination of some orphaned yeast sequences that could be
mapped to Gene ID, we found that they were orphans because they differed in
one or two amino acids from that protein’s reference representative in RefSeq
but were not clustered with that representative’s Gene record. Additional
mappings can be found through database cross-reference from a Uniprot record
pointing to a Gene ID. The iRefIndex canonicalization procedure captures some
of these associations in the mappings.txt file but they are not available in
the main iRefIndex MITAB files. We have found (Supplementary Table 5) that
some CROGIDs (mostly in human) can be additionally mapped by using this
information in the mappings.txt file. Notably, ppiTrim accesses a more recent
version of Uniprot then iRefIndex and is thus able to find more mappings by
accessing Uniprot cross-references directly. Finally, there is a substantial
number of Uniprot records that do not have a cross-reference to NCBI Gene but
can be linked to a Gene record through their canonical gene names. This last
approach can be suggested as an improvement for iRefIndex canonicalization
processing.

Around 10% of CROGIDs could not be mapped to Gene IDs even after processing
with ppiTrim algorithms. A few interactors (Supplementary Table 5) have only
PDB accessions as their primary IDs since their interactions were derived from
crystal structures. In such cases, often only partial sequences of
participating proteins are available. These partial sequences cannot be fully
matched to any Uniprot or RefSeq record and hence are assigned a separate ID.
Hence, an improvement for our procedure, that would account for this case as
well as for those unmapped proteins that differ from canonical sequences only
by few amino acids, would be to use direct sequence comparison to find the
closest valid reference sequence. This task may not be technically difficult
(a similar procedure was applied by Alves _et al._ (34) to construct protein
databases for mass spectrometry data analysis) but is beyond the scope of
ppiTrim, which is intended as a relatively short standalone script. In our
opinion, such additional mappings would best be performed at the level of
reference sequence databases such as Uniprot or RefSeq, which contain curator
expertise to resolve ambiguous cases.

Table 4: Deflating spoke-expanded complexes  Species | Publications | Pairs | Complexes
---|---|---|---
initial | remaining |  C |  G |  R |  A |  N
S. cerevisiae | 3924 | 118819 | 28643 |  7729 |  323 |  5384 |  3190 |  1311
H. sapiens | 10317 | 56111 | 35650 |  8382 |  181 |  1143 |  1443 |  304
D. melanogaster | 398 | 1722 | 1053 |  220 |  16 |  82 |  33 |  3
Shown are the numbers of complexes obtained by deflating binary interactions
with affinity chromatography (or related) as experimental method. Types of
complexes are indicated by one letter codes described in Table 1. The counts
of pairs shown include those from publications with fewer than three
interactions (per database), which could never be deflated into complexes.

Protein complexes obtained through chromatography techniques provide
information complementary to direct binary interactions. While it is often
difficult to determine the exact layout of within-complex pairwise
interactions, an identification of an association of several proteins using
mass spectroscopy is an evidence for in vivo existence of that association.
Unfortunately, in spite of its great importance, the currently available
information within iRefIndex is deficient because of different treatments of
complexes by different source databases. Our results (Table 4) show that the
apparently inflated complexity of interaction datasets can be substantially
reduced by attempting to collapse spoke-expanded complexes. For yeast, this
results in almost three quarters reduction of the number of candidate
interactions. The majority of new complexes falls into ‘G’ and ‘R’ categories,
which can be considered most reliable. For the human dataset, reduction is
small as a proportion although in absolute terms the number of new complexes
is over 3000. The fruitfly dataset did not contain many candidate interactions
or complexes and hence not many new complexes were obtained.

In general, it is difficult to assess whether newly generated complexes from
‘A’ and ‘N’ categories are biologically justified, that is, whether they
represent a functional entity. If a bait and its preys genuinely originate
from a single experiment, they definitely form a physical association that may
be a part of or an entire functional complex. Since ppiTrim preserves the
experimental role labels and the original interaction identifiers, little
information is lost by deflating such associations into a single record. On
the other hand, for some publications, especially those involving experiments
with ubiquitin-like proteins as bait, each bait-prey association may represent
a separate experiment and it does not substantiate that different prey
proteins may be co-present in the cell. For example, BioGRID provides 158
physical associations from the paper by Hannich _et al._ (35), each involving
the yeast Smt3p (SUMO, a ubiquitin-like) protein as a bait. In this case, it
is not true that all the involved preys together form a large complex with the
bait. ppiTrim avoids this particular case by not deflating potentially too
large complexes (the maximum deflated complex size is tunable by the user with
the default of 120 proteins), but one can assume that some of deflated
‘complexes’ do not exist in vivo.

To more closely investigate the fidelity of generated complexes, we randomly
sampled 25 ‘A’ and ‘N’ deflated yeast complexes from the final output of
ppiTrim and examined their original publications. Out of these 25 complexes,
15 originated from high-throughput publications (mostly Gavin _et al._ (31)
and Krogan _et al._ (32) – Supplementary Table 6), while 10 came from small
experiments (Supplementary Table 7). In all high-throughput cases, the
deflated complex represents a true experimental association. In the cases when
authors present their own derived complexes, which in many cases can be found
separately under the ‘C’ category, our deflated complexes form parts of larger
derived complexes. Indeed, such derived complexes are obtained by assembling
the results of several bait-prey experiments, each of which forms a single
deflated complex. The results are more varied for low-throughput publications.
In most cases, deflated complexes clearly correspond to functional complexes,
although it is sometimes difficult to fully relate author’s conclusions with
their reported results. In two cases, the inferred association is incorrect
due to curation errors in the original database. We have also found a single
case where the publication authors directly state that proteins in a deflated
complex do not form a stable complex.

While our sample is extremely small, it does indicate several issues arising
from deflation of bait-prey relationships. In most cases, deflated complexes
form parts of what are believed to be functional complexes. It appears that
curation errors or ambiguities may be a more significant source of wrongly
inferred associations than our main assumption that a bait with several preys
in a single publication represents a single unit. Overall, we feel that the
benefits from reduction of interactome complexity outweigh the disadvantages
from potentially over deflating interactions. The best way to solve the
problem of different representations of protein complexes would be at the
level of source databases (BioGRID in particular), by reexamining the original
publications. Our complexes from the ‘R’ category, where deflated complexes
fully agree with an annotated complex from a different database, could serve
as a guide in this case.

Table 5: Final consolidated datasets Species | Publications | Input Pairs | Consolidated | Conflicts
---|---|---|---|---
biochem | other | complexes | directed | undirected | resolvable | unresolvable
S. cerevisiae | 6303 | 5780 | 119329 | 10778 | 5525 | 63648 | 19344 | 454
H. sapiens | 22660 | 2446 | 199094 | 6483 | 2042 | 85480 | 26478 | 1333
D. melanogaster | 564 | 51 | 111862 | 227 | 33 | 27981 | 19430 | 11
For each species, shown are the numbers of input pairs (input complexes are those from Table 4), classified as either biochemical reactions (potentially directed) or others; also shown are the final numbers of consolidated interactions (classified as complexes, directed or undirected). The ‘other’ column accounts only for those interactions that were not deflated into complexes in Phase II. The last two columns show the total numbers of resolvable and unresolvable conflicts between consolidated interactions. An unresolvable conflict is an instance where two consolidated interactions, originated from the same publication, are reported using incompatible experimental detection method labels by different databases. A resolvable conflict is the case where source interactions within a single consolidated interaction have different (but compatible) experimental detection method labels. Table 6: Most common interaction detection method PSI-MI term conflicts  Term A |  Sources A |  Term B |  Sources B | Counts
---|---|---|---|---
MI:0007 (anti tag coimmunoprecipitation) |  M |  MI:0676 (tandem affinity purification) |  DI | 132
MI:0004 (affinity chromatography) |  B |  MI:0363 (inferred by author) |  I | 60
MI:0018 (two hybrid) |  DIMN |  MI:0096 (pull down) |  BI | 43
MI:0071 (molecular sieving) |  DIN |  MI:0096 (pull down) |  B | 32
MI:0030 (cross-linking study) |  DIMN |  MI:0096 (pull down) |  B | 22
MI:0007 (anti tag coimmunoprecipitation) |  IM |  MI:0676 (tandem affinity purification) |  DI | 1227
MI:0018 (two hybrid) |  BDHIM |  MI:0096 (pull down) |  BM | 17
MI:0096 (pull down) |  B |  MI:0107 (surface plasmon resonance) |  DM | 6
MI:0008 (array technology) |  I |  MI:0049 (filter binding) |  M | 5
MI:0019 (coimmunoprecipitation) |  IM |  MI:0096 (pull down) |  BI | 5
Top five most common interaction detection method PSI-MI term unresolvable
conflicts for yeast (top) and human (bottom) datasets are shown. Source
databases are indicated by one letter codes B (BioGRID), D (DIP), I (IntAct),
H (HPRD), M (MINT), P (MPPI).

Overall, our processing significantly reduced the number of interactions
within each of the three datasets considered (Table 5). This indicates a
significant redundancy, particularly for protein complexes, original and
deflated (compare Table 4 with Table 5), and for binary interactions. The
directed interactions (biochemical reactions) are relatively rarer and largely
non-redundant at this stage. Given their importance in elucidating biological
function, the directed interactions are expected to be discovered more fully
with time. However, one should note that PSI-MI format can only represent a
static relationship among a set of physical entities involved in the same
event, but cannot actually represent two sides of a reaction e.g. $A+B\to
C+D$. Certain pairs of PSI-MI biological role terms can be combined to
represent interaction direction e.g. ‘enzyme’ and ‘enzyme target’, but these
are weak compared to the rich ways that pathway databases like Reactome (36)
represent events.

To demonstrate the utility of our conflict resolution method, we present the
counts for resolvable and unresolvable conflicts in Table 5. Resolvable
conflicts significantly outnumber the unresolvable ones. Examining the most
common examples of resolvable conflicts (Supplementary Table 8), one can see
that a majority of them indeed represent the same experiment. Possible
exceptions are human interactions annotated by HPRD, which have ambiguous
detection method labels. To address this and similar problems, ppiTrim
provides the maxsources confidence score (Supplementary Table 1), which is an
estimate of the maximal number of independent experiments contributing to a
consolidated interaction. An interesting example of a resolvable conflict in
Supplementary Table 8 is the 444 instances of a consolidated interaction
containing source interactions with detection method labels MI:0004 (affinity
chromatography technology), MI:0007 (anti tag coimmunoprecipitation), and
MI:0676 (tandem affinity purification). This case is very similar to the one
described in Figure 2: the last two terms are incompatible but the first
resolves the conflict as the finest consistent term.

Upon closer examination of the few unresolvable conflicts (Table 6), it can be
seen that most common conflicts arise as instances of few specific labeling
disagreements between databases. In many cases, such disagreements arise from
using different sub-terms of affinity chromatography (see Fig. 2) and can be
resolved by assigning a more general term consistent with both conflicting
terms. In many other cases, the conflicts are due to BioGRID internally using
a more restricted detection method vocabulary than the IMEx databases (DIP,
IntAct and MINT). However, in some rare cases, an unresolvable conflict arises
when different databases annotate different experiments from the same
publication. For example, each of DIP, BioGRID and IntAct report several raw
interactions from the paper by Blaiseau and Thomas (37) (pubmed:9799240),
where yeast Met4p protein interacts with each of Met28p, Met31p and Met32p in
binary interactions. The paper reports several experiments using different
techniques including northern blotting, yeast two hybrid and electrophoretic
mobility shift assays. For the interaction between Met4p and Met28p, BioGRID
and IntAct report only MI:0018 (yeast two hybrid) method, while DIP reports
only MI:0404 (comigration in non denaturing gel electrophoresis), resulting in
unresolvable conflict. Hence, in this case, each database on its own provides
incomplete evidence for this interaction.

The ppiTrim algorithms work best if accurate and fully populated fields for
interaction detection method, publication and interaction type are available
in its input dataset. This requirement is mostly fulfilled. Nevertheless, we
have noticed two minor inconsistencies. The first, which will be fixed in a
subsequent release of iRefIndex (Ian Donaldson, private communication),
involves the PSI-MI labels for interaction detection method for CORUM
interactions and complexes. These are missing from iRefIndex although they are
present in the original CORUM source files. The second issue concerns missing
or invalid Pubmed IDs for certain interactions. We found that a number of
interactions with missing Pubmed IDs come from MINT. Upon inspection of the
original MINT files, we discovered that in many cases MINT supplies a Digital
Object Identifier (DOI) for a publication as its identifier instead of a
Pubmed ID (although the corresponding Pubmed ID can be obtained from the MINT
web interface). To ensure consistency with other source databases within
iRefIndex, it would be desirable to have the Pubmed IDs available for these
interactions as well.

In this paper, we have identified the tasks needed for using combined
interaction datasets provided by iRefIndex as a basis for construction of
reference networks and developed a script to process them into consistent
consolidated datasets. We see ppiTrim as answering a temporary need for a
consolidated database and hope that most of the issues that required
processing will be eventually fixed in upstream databases and distributed
through IMEx consortium. At this stage we have not addressed the issue of
quality of interactions although such information is available in some
databases for some publications (23). Utilizing the quality information in
consolidating datasets demands a universal data-quality measure that is not
yet existent.

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## Acknowledgments

This work was supported by the Intramural Research Program of the National
Library of Medicine at the National Institutes of Health. We thank Dr.
Donaldson for his critical reading of this manuscript and for providing us
with the proprietary version of iRefIndex 7.0 dataset, which was used for
initial development of ppiTrim.

Supplementary Materials for ‘ppiTrim: constructing non-redundant and up-to-
date interactomes’

Aleksandar Stojmirović, and Yi-Kuo Yu

National Center for Biotechnology Information
National Library of Medicine
National Institutes of Health
Bethesda, MD 20894
United States

Supplementary Table 1: Description of ppiTrim MITAB 2.6 columns  Column | Short Name |  Description |  Example
---|---|---|---
1 | uidA |  Smallest Gene ID of the interactor A∗† |  entrezgene/locuslink:854647
2 | uidB |  Smallest Gene ID of the interactor B∗ |  entrezgene/locuslink:855136
3 | altA |  All gene IDs of the interactor A∗ |  entrezgene/locuslink:854647
4 | altB |  All gene IDs of the interactor B∗ |  entrezgene/locuslink:855136
5 | aliasA |  All canonical gene symbols and integer CROGIDs of interactor A |  entrezgene/locuslink:BNR1| icrogid:2105284
6 | aliasB |  All canonical gene symbols and integer CROGIDs of interactor B |  entrezgene/locuslink:MYO5| icrogid:3144798
7 | method |  PSI-MI term for interaction detection method |  MI:0018(two hybrid)
8 | author |  First author name(s) of the publication in which this interaction has been shown‡ |  Tong AH [2002]|tong-2002a-3
9 | pmids |  Pubmed ID(s) of the publication in which this interaction has been shown |  pubmed:11743162
10 | taxA |  NCBI Taxonomy identifier for interactor A |  taxid:4932(Saccharomyces cerevisiae)
11 | taxB |  NCBI Taxonomy identifier for interactor B |  taxid:4932(Saccharomyces cerevisiae)
12 | interactionType |  PSI-MI term for interaction type |  MI:0407(direct interaction)
13 | sourcedb |  PSI-MI terms for source databases‡ |  MI:0000(MPACT)|MI:0463(grid)| MI:0465(dip)|MI:0469(intact)
14 | interactionIdentifier |  A list of interaction identifiers⋆ |  ppiTrim:tyuGkSOK231dh3YnSi6GbczJCFE=| MPACT:8233|dip:DIP-11198E|grid:147506| intact:EBI-601565|intact:EBI-601728| irigid:288990|edgetype:X
15 | confidence |  A list of ppiTrim confidence scores∙ |  maxsources:2|dmconsistency:full| conflicts:S3oaiXt5tA4vVrUsO1rc1TA9krk=
16 | expansion |  Either ‘none’ for binary interactions or ‘bipartite’ for subunits of complexes |  none
17 | biologicalRoleA |  PSI-MI term(s) for the biological role of interactor A‡ |  MI:0499(unspecified role)
18 | biologicalRoleB |  PSI-MI term(s) for the biological role of interactor B ‡ |  MI:0499(unspecified role)
19 | experimentalRoleA |  PSI-MI term(s) for the experimental role of interactor A‡ |  MI:0496(bait)|MI:0498(prey)| MI:0499(unspecified role)
20 | experimentalRoleB |  PSI-MI term(s) for the experimental role of interactor B‡ |  MI:0496(bait)|MI:0498(prey)| MI:0499(unspecified role)
21 | interactorTypeA |  PSI-MI term for the type of interactor A (either ‘protein’ or ‘protein complex’) |  MI:0326(protein)
22 | interactorTypeB |  PSI-MI term for the type of interactor B (always ‘protein’) |  MI:0326(protein)
29 | hostOrganismTaxid |  NCBI Taxonomy identifier for the host organism |  taxid:4932(Saccharomyces cerevisiae)
31 | creationDate |  Date when ppiTrim was run |  2011/05/11
32 | updateDate |  Date when ppiTrim was run |  2011/05/11
35 | checksumInteraction |  ppiTrim ID for an interaction |  ppiTrim:tyuGkSOK231dh3YnSi6GbczJCFE=
36 | negative |  Always ‘false’ |  false
The above table shows short descriptions for the columns of lines output by ppiTrim with examples. The columns that are not used by ppiTrim (- output) are omitted. List of items are always separated by the $|$ character (without any intervening spaces). This description only applies to ppiTrim output; the full PSI-MI 2.6 TAB format description can be found at http://code.google.com/p/psimi/wiki/PsimiTab26Format Notes: ∗An interactor may be associated with several Gene IDs. In that case the smallest one is written in uid columns while the entire list is shown in alt columns. †Interactor A may be used to denote a protein complex. In that case the uidA is of the form complex:$<$ppiTrim ID$>$, while altA and aliasA are left empty. ‡Multiple items are possible, originating from all source records contributing to the consolidated interaction. ⋆First ID is always the ppiTrim ID for the consolidated interaction, followed by the original IDs for all contributing interactions and their integer RIGIDs from iRefIndex. The final item is the edge type code. ∙maxsources: an estimate of the maximal number of independent experiments contributing to the consolidated interaction; dmconsistency: consistency of contributing detection method terms. Values are one of invalid (no method terms present), single (only one method term), min (minimum term found but not maximum), max (maximum term found but not minimum), and full (both minimum and maximum term present in subcluster); conflicts: ppiTrim IDs of consolidated interactions with detection method term in conflict with the current one. Supplementary Table 2: Remapping of obsolete PSI-MI terms  Original Term | Mapped Term | Notes
---|---|---
MI:0021 |  colocalization by fluorescent probes cloning |  MI:0428 |  imaging technique |
MI:0022 |  colocalization by immunostaining |  MI:0428 |  imaging technique | $\ast$
MI:0023 |  colocalization/visualisation technologies |  MI:0428 |  imaging technique | $\ast$
MI:0025 |  copurification |  MI:0401 |  biochemical |
MI:0059 |  gst pull down |  MI:0096 |  pull down |
MI:0061 |  his pull down |  MI:0096 |  pull down |
MI:0079 |  other biochemical technologies |  MI:0401 |  biochemical |
MI:0109 |  tap tag coimmunoprecipitation |  MI:0676 |  tandem affinity purification |
MI:0045 |  experimental interaction detection |  MI:0492 |  in vitro | $\dagger$
MI:0493 |  in vivo |  MI:0493 |  in vivo | $\dagger$
MI:0000 |  coip coimmunoprecipitation |  MI:0019 |  coimmunoprecipitation | $\star$
MI:0000 |  elisa enzyme-linked immunosorbent assay |  MI:0411 |  enzyme linked immunosorbent assay | $\star$
$\ast$ Interaction type is also adjusted to MI:0403 as recommended in psi-mi.obo; $\dagger$ HPRD terms are treated as a special case, see main text; $\star$ MPPI interactions in the human dataset. Supplementary Table 3: Mapping PTM labels from BioGRID into PSI-MI terms Original Term | Mapped Term
---|---
Acetylation |  MI:0192 | acetylation reaction
Deacetylation |  MI:0197 | deacetylation reaction
Demethylation |  MI:0871 | demethylation reaction
Dephosphorylation |  MI:0203 | dephosphorylation reaction
Deubiquitination |  MI:0204 | deubiquitination reaction
Glucosylation |  MI:0559 | glycosylation reaction
Methylation |  MI:0213 | methylation reaction
Nedd(Rub1)ylation |  MI:0567 | neddylation reaction
No Modification |  MI:0414 | enzymatic reaction
Phosphorylation |  MI:0217 | phosphorylation reaction
Prenylation |  MI:0211 | lipid addition
Proteolytic Processing |  MI:0570 | protein cleavage
Ribosylation |  MI:0557 | adp ribosylation reaction
Sumoylation |  MI:0566 | sumoylation reaction
Ubiquitination |  MI:0220 | ubiquitination reaction
Supplementary Table 4: Processing source interactions (RIGIDs) Species | Initial | Without Gene ID | Retained | With Mapped Gene ID
---|---|---|---|---
S. cerevisiae | 186530 | 1272 | 79931 | 591
H. sapiens | 138570 | 1917 | 84860 | 7158
D. melanogaster | 46925 | 4988 | 39200 | 2176
Statistics of initial processing of raw interactions from in terms of iRefIndex RIGIDs. A RIGID for an interaction is a unique hash derived from its interactants’ sequences (with order not significant). Thus, multiple interactions with the same interactants share the same RIGID. Shown are the initial number, number removed due to missing Gene ID, total number of retained and the number retained containing at least one interactant with mapped Gene ID. Compared to Table 2 in the main text, this table does not contain a column showing the number of removed RIGIDs due to filtering criteria. This is becuase the ppiTrim filtering routine operates on raw interactions (corresponding to a single record from a source database) and some RIGIDs would be associated with both accepted and removed raw interactions. Supplementary Table 5: Mapping CROGID identifiers from iRefIndex into Gene IDs: details Species | I | V | O | R | P | T | M | G | S | B
---|---|---|---|---|---|---|---|---|---|---
S. cerevisiae | 5552 | 0 | 0 | 607 | 95 | 461 | 0 | 26 | 21 | 386
H. sapiens | 11428 | 11 | 0 | 2615 | 155 | 2017 | 71 | 754 | 429 | 0
D. melanogaster | 7780 | 0 | 30 | 1569 | 18 | 814 | 2 | 124 | 440 | 0
Detailed statistics of mapping CROGIDs into Gene IDs. All numbers denote CROGIDs: directly mapped to valid Gene IDs in the iRefIndex file (I); directly mapped to Gene IDs but the Gene IDs were updated during validation (V); directly mapped to obsolete Gene IDs (O); not directly mapped to Gene IDs – total orphans (R); orphans with PDB accession as a primary ID (P); orphans with Uniprot accession as a primary ID (T); additionally mapped to a valid Gene ID using mapping.txt file from iRefIndex (M); additionally mapped to a valid Gene ID using a direct reference from Uniprot record (G); additionally mapped to a valid Gene ID using a gene name from Uniprot record (S); additionally mapped to a Gene ID that was not valid (B). Supplementary Table 6: Randomly sampled deflated complexes from high throughput publications ppiTrim Complex ID | Sources | Pubmed ID |  Members |  Comments
---|---|---|---|---
8AVRUHG76vkiFn2cZGICNZzr00Y= | grid | 14759368 |  CFT2, YSH1, PTA1, MPE1 |  Part of mRNA cleavage/polyadenylation complex (4/10 proteins).
9yS57j/gbRbOlNmmimsVeonoraA= | grid | 14759368 |  NUT1, MED7, MED4, SIN4, SRB4 |  Part of mediator complex.
JU+EOkq6ipLh9DJKRtGRLUvT7vM= | grid,mint | 14759368 |  UBP6, RPT3, RPN9, RPT1, RPN8, RPN2, RPN7, RPN1 |  Part of proteasome. MINT does not contain complexes from the original paper.
HtTmhGiPyfIT2vFtRZ94uWw0rsY= | grid | 16429126 |  IOC3, HTB1, HTA2, HHF2, ISW1, KAP114, ITC1, RPS4A, VPS1, NAP1, RPO31, ISW2, TBF1, BRO1, MOT1 |  Part of Complex # 99.
LnNzfyPGShcG7zkKynU6+fsK2eU= | grid | 16429126 |  PSK1, NTH1, BMH2, RTG2, BMH1 |  Part of complex # 147 (two core proteins plus three attachments).
S2I6VRjFMWC6rkkM+oYXwKCg9YQ= | grid | 16429126 |  RPL4B, MNN10, MNN11, HOC1, MNN9, ANP1 |  Core complex (# 111 – mannan polymerase II) + one attachment protein (RPL4B).
1fRmAapl2ruoQq202YUJg55maFo= | grid,mint | 16554755 |  RSM24, RSM28, MRPS5, MRP13, MRPS35, RSM27, RSM7, RSM25, MRPS17, MRPS12, RSM19, MRP4 |  Part of complex # 1.
5tBkYOmK/G1h3vaQmiOnUoBHHMQ= | grid,mint | 16554755 |  CFT2, YSH1, MPE1, PAP1 |  Part of complex # 18.
9f2DVj2rDGeCP53LHOnWRMwq14A= | grid,mint | 16554755 |  KAP95, RTT103, VMA2, RAI1, RAT1, RPB2, SRP1 |  True experimental association but not part of any derived complex.
AVawv51+6Fqe3DquygD/XfyrXxE= | grid,mint | 16554755 |  RRP42, RRP45, RRP6, CSL4, MPP6, RRP4, LRP1, DDI1 |  Part of complex # 19.
NOLEwovavMsFrQEdkSUt/mldeMc= | grid,mint | 16554755 |  CDC3, SHS1, CDC11, CDC12 |  Part of complex # 121.
WA51i87Lj1wGp/EeF1OV/YvbW1Y= | grid,mint | 16554755 |  GTT2, TRX1, CRN1, SSA3, IPP1, CMD1, TRX2, TDH1, RPL40B, CDC21, OYE2 |  True experimental association but not part of any derived complex.
YN/hQXQvzoB5HqrgPzVth28mGsY= | grid,mint | 16554755 |  RRP43, RRP42, RRP45, RRP40, DIS3, RRP6, RRP4, LRP1 |  Part of complex # 19.
1LRk+AgI8HpGOSAgkhDzNJWSvtI= | grid | 20489023 |  RTG3, RTG2, TOR1, TOR2, CKA2, MYO2, MKS1, KOG1 |  True experimental association.
xWzvxeJFGqjkCihjmQVf5gZhJjQ= | dip,grid,mint | 20489023 |  PUF3, SAM1, GCD6, SPT16, MTC1, YGK3, LSM12 |  True experimental association.
To partially investigate the fidelity of deflated complexes of type A and N, we randomly sampled 25 such complexes from the final ppiTrim yeast dataset and examined the original publications associated with them. This table contains 15 deflated complexes from high-throughput publications, while Supplemenary Table 7 contains the complexes from low-throughput publications. Most of high-throughput papers referred to in this table present both the lists of bait-prey associations and of derived complexes. The complexes delated by ppiTrim are often derived from the former and form only parts of the latter. In the last column of this table, the complex numbers referred to are labels used by the publication’s authors. Supplementary Table 7: Randomly sampled deflated complexes from low-throughput publications ppiTrim Complex ID | Sources | Pubmed ID |  Members |  Comments
---|---|---|---|---
15VfQtoe5gxGNwPSY3AG0sq6A2U= | grid | 9891041 |  CCR4, HPR1, PAF1, SRB5, GAL11 |  NOT a true complex. This is because of bad annotation of PAF1–SRB5 interaction by the BioGRID. Completely opposite interpretation was given in the paper.
d79IdtwfTAENrH8CQ+c8CpS389Y= | grid | 10329679 |  YPT1, VPS21, YPT7, GDI1 |  True complex. This is the only experiment in the paper.
EtS4cgphEpTqJb/FS5qxyzf0ke8= | grid | 11733989 |  CDC39, CCR4, CDC36, CAF130, CAF40, CAF120, POP2, NOT5, MOT2 |  True complex. CAF120 is an unusual member that could almost be left out.
2kOyGdwzWywSpN5mhK26gCcC6LQ= | grid | 14769921 |  GBP2, IMD3, TEF1, KEM1, CTK2, CTK1, CTK3 |  True complex, except that TEF1 should be TEF2. This is an error in the iRefIndex source file; the BioGRID website has the correct assignment.
Kd07BBUF07Sqy9NP3D0lixsS/TY= | grid | 15303280 |  BUD31, RPL2B, PRP19, CDC13, ATP1, RPS4A, SNU114, MDH1, MAM33, MRPL3, MRPL17, PRP8, PRP22, PAB1, BRR2 |  True association
ZAGz/IZqkEr3/NTDLzPEDAD9cKo= | grid | 16179952 |  CDC40, UFD1, SSM4, UBX2 |  NOT a true complex, probably due to a typo in annotation. CDC40 cannot be found anywhere in the paper and should most likely be CDC48.
RDu0dsPAN0QEadfSU5sv05Ifihw= | grid | 16286007 |  SIN3, RCO1, RPD3, UME1, EAF3 |  True complex.
Vqbn3dDwTPgyE9DzbatFNqzdFe0= | grid | 16615894 |  VPS36, VPS25, VPS28, SNF8 |  Vps28 binds the other three, which form a complex.
lmdypAN9kaHBdasLWS19x8K7KkE= | grid | 20159987 |  UBI4, UFD2, PEX29, SSM4 |  Biological association but indicated as ‘NOT a stable complex’ in the paper.
aakRh6qVahGxGvqHe399+faxPvA= | grid | 20655618 |  PEX13, PEX10, PEX8, PEX12 |  Association is correct, although mutant strain was used to obtain this particular complex.
To partially investigate the fidelity of deflated complexes of type A and N, we randomly sampled 25 such complexes from the final ppiTrim yeast dataset and examined the original publications associated with them. This table contains 10 deflated complexes from low-throughput publications, while Supplemenary Table 6 contains the complexes from high-throughput publications. Supplementary Table 8: Summary of resolvable conflicts  Consolidated terms | Count
---|---
MI:0018 (two hybrid), MI:0045 (experimental interaction detection), MI:0398 (two hybrid pooling approach), MI:0399 (two hybrid fragment pooling approach) | 3959
MI:0090 (protein complementation assay), MI:0111 (dihydrofolate reductase reconstruction) | 2612
MI:0090 (protein complementation assay), MI:0112 (ubiquitin reconstruction) | 2077
MI:0004 (affinity chromatography technology), MI:0676 (tandem affinity purification) | 1840
MI:0004 (affinity chromatography technology), MI:0007 (anti tag coimmunoprecipitation) | 1408
MI:0018 (two hybrid), MI:0045 (experimental interaction detection), MI:0397 (two hybrid array) | 1231
MI:0018 (two hybrid), MI:0045 (experimental interaction detection) | 954
MI:0018 (two hybrid), MI:0397 (two hybrid array) | 914
MI:0045 (experimental interaction detection), MI:0686 (unspecified method) | 628
MI:0004 (affinity chromatography technology), MI:0019 (coimmunoprecipitation) | 598
MI:0018 (two hybrid), MI:0398 (two hybrid pooling approach) | 506
MI:0004 (affinity chromatography technology), MI:0007 (anti tag coimmunoprecipitation), MI:0676 (tandem affinity purification) | 444
MI:0018 (two hybrid), MI:0045 (experimental interaction detection), MI:0686 (unspecified method) | 320
MI:0004 (affinity chromatography technology), MI:0096 (pull down) | 217
MI:0415 (enzymatic study), MI:0424 (protein kinase assay) | 192
MI:0045 (experimental interaction detection), MI:0081 (peptide array) | 150
MI:0045 (experimental interaction detection), MI:0676 (tandem affinity purification) | 120
MI:0492 (in vitro), MI:0493 (in vivo) | 5739
MI:0018 (two hybrid), MI:0398 (two hybrid pooling approach) | 5394
MI:0018 (two hybrid), MI:0492 (in vitro), MI:0493 (in vivo) | 2796
MI:0096 (pull down), MI:0492 (in vitro), MI:0493 (in vivo) | 2760
MI:0096 (pull down), MI:0492 (in vitro) | 2134
MI:0018 (two hybrid), MI:0492 (in vitro) | 1658
MI:0018 (two hybrid), MI:0493 (in vivo) | 1193
MI:0018 (two hybrid), MI:0397 (two hybrid array) | 1045
MI:0096 (pull down), MI:0493 (in vivo) | 513
MI:0004 (affinity chromatography technology), MI:0006 (anti bait coimmunoprecipitation) | 384
MI:0004 (affinity chromatography technology), MI:0019 (coimmunoprecipitation) | 309
MI:0004 (affinity chromatography technology), MI:0007 (anti tag coimmunoprecipitation) | 195
MI:0114 (x-ray crystallography), MI:0492 (in vitro) | 166
MI:0004 (affinity chromatography technology), MI:0096 (pull down) | 161
MI:0047 (far western blotting), MI:0492 (in vitro), MI:0493 (in vivo) | 106
MI:0018 (two hybrid), MI:0398 (two hybrid pooling approach) | 17738
MI:0018 (two hybrid), MI:0399 (two hybrid fragment pooling approach) | 1426
All resolvable conflicts with counts of more than 100 for yeast (top), human
(middle) and fruitfly (bottom) datasets are shown.