Predicting Gene Functions from Text Using a Cross-Species Approach

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Predicting Gene Functions from Text Using a Cross-Species Approach. Emilia Stoica and Marti Hearst School of Information University of California, Berkeley. Research Supported by NSF DBI-0317510 and a gift from Genentech. Goal.
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Predicting Gene Functions from Text Using a Cross-Species ApproachEmilia Stoica and Marti HearstSchool of InformationUniversity of California, BerkeleyResearch Supported by NSF DBI-0317510 and a gift from GenentechGoal Annotate genes with functional information derived from journal articles.Gene Ontology (GO)
  • Gene Ontology (GO) controlled vocabulary for functional annotation
  • ~ 17,600 terms (circa July 2004)
  • Organized into 3 distinct acyclic graphs
  • molecular functions
  • biological processes
  • cellular locations
  • More general terms are “parents” of less general terms:
  • development(GO:0007275) is the parent of embryonic development(GO:0001756)
  • Challenges
  • GO tokens might not appear explicitly
  • Example: PubMed 10692450GO:0008285:negative regulation of cell proliferation Occurs as:inhibition of cell proliferation
  • GO tokens might not occur contiguously
  • Example: PubMed 10734056, GO:0007186: G-protein coupled receptor protein signaling pathway Occurs as: Results indicate that CCR1-mediated responses are regulated …in the signaling pathway, by receptor phosphorylation at the level of receptor G/proteincoupling … CCR1 binds MIP-1 alpha.Challenges
  • The simplest strategy (assigning GO codes to genes simply because the GO tokens occur near the gene) yields a large number of false positives.
  • Issues:
  • The text does not contain evidence to support the annotation,
  • The text contains evidence for the annotation, but the curator knows the gene to be involved in a function that is more general or more specific than the GO code matched in text.
  • Challenges
  • GO contains hints about what kinds of evidence are required for annotation, e.g.:
  • The text should mention co-purification, co-immunoprecipitationexperiments
  • Requiring these evidence terms does not seem to improve algorithms.
  • Related Work
  • Mainly in the context of BioCreative competition (2004)
  • Chiang and Yu 2003, 2004:
  • Find phrase patterns commonly used in sentences describing gene functions
  • (e.g., “gene plays an important role in”, “gene is involved in”)
  • Final assignments made with a Naïve Bayes classifier
  • Ray and Craven 2004, 2005:
  • Learn a statistical model for each GO code (which words are likely to co-occur in the paragraphs containing GO codes);
  • Decide among candidates via a multinomial Naïve Bayes classifier
  • Rice et al. 2004:
  • Train an SVM for each GO code.
  • Target genes assigned best-scoring GO code.
  • Related Work, cont.
  • Couto et al. 2004
  • Determine if the “information content” of the matching GO terms is larger than for all the candidate GO terms.
  • Verspoor et al. 2004
  • Expand GO tokens with words that frequently co-occur in a training set; use a categorizer that explores the structure of the Gene Ontology to find best hits.
  • Ehler and Ruch 2004:
  • Treat each document as a query to be categorized
  • Create a score based on a combination of pattern matching and TF*IDF weighting
  • Annotate gene with top-scoring GO codes.
  • Our Approach
  • Two main contributions:
  • Use cross-species information (CSM)
  • Check for biological (in) consistencies (CSC)
  • Cross-Species MatchMain Idea
  • Use orthologous genes
  • [Genes of different species that have evolved directly from a common ancestor.]
  • Assumption:
  • Since there is an overlap between the genomes of the two species, their orthologs may share some functions, and consequently some GO codes
  • Idea: to predict GO codes for target genes in target species, use the GO codes assigned to their orthologous genes
  • We use Mouse vs. Human genes
  • General procedure
  • Analyze text at sentence level
  • Eliminate stop words, punctuation characters and divide the text into tokens using space as delimiter
  • Normalize and match different variations of gene names using the algorithm of Bhalotia et al.’03
  • For every sentence that contains the target gene:
  • A GO code is matched if the sentence contains a percentage of GO tokens larger than a threshold (0.75 for CSM and 1 for CSC)
  • Cross Species Match Algorithm
  • CSM(g, a): For a target gene g, search in article a for only the GO codes annotated to its ortholog
  • If at least 75% of the GO code terms are found in a sentence containing the gene name, the code is matched.
  • Note: we must eliminate annotations of orthologs marked with IEA and ISS codes to avoid circular references.
  • Cross-Species Correlation Main Idea
  • Observation:
  • Since GO codes indicate gene function, it is logical for some to often co-occur in annotations and for others to rarely do so.
  • Assumption:
  • If one GO code tends to occur in the orthologous genes’ annotations when another one does not, then assume the second is not a valid assignment for the target species
  • Example:
  • If text seems to contain evidence for rRNA transcription (GO:0009303) nucleolus(GO:0005737) and extracellular(GO:0005576), then extracellular is suspicious.
  • The algorithm identifies the “suspicious” cases.
  • Cross-Species Correlation Algorithm
  • For every pair of GO codes in the orthologous genes database, compute a X2coefficient.
  • N: the total number of GO codes
  • O11: # of times the ortholog is annotated with both GO1 and GO2
  • O12: # of times the ortholog is annotated with GO1 but not GO2
  • O21: # of times the ortholog is annotated with GO2 but not GO1
  • O12: # of times the ortholog is not annotated with GO1 or GO2
  • X2Cross-Species Correlation Algorithm
  • M(g,a) = GO codes matched in article a for gene g
  • O(g) = GO codes assigned to the ortholog of g
  • o = size of O(g), p = percentage (0.2)
  • For every potentially matching GO code GO1 in M(g,a)
  • For every GO code GO2 in O(g)
  • Count how often X2(GO1,GO2) is significant
  • If this count is < p*o then assume GO1 is not valid.
  • Else assign GO1 to g
  • Information FlowEvaluation using BioCreative
  • Task 2.2:
  • Annotate 138 human genes with GO codes using 99 full text articles;
  • For each annotation, provide the passage of text that the annotation was based upon.
  • Annotations from participants were manually judged by human curators
  • A prediction was considered “perfect” if the text passage
  • contained the gene name, and
  • provided evidence for annotating the gene with the GO code
  • Results on BioCreative
  • Our research was conducted after the competition had past, so our annotations could not be judged by the same curators
  • Used the “perfect predictions”
  • (unfair to our system; ignores relevant predictions we find that other systems do not)
  • Our prediction is correct if it matches a perfect prediction (e.g., vhl is annotated with transcription(GO:0006350) in PubMed 12169961 “vhl inhibits transcription elongation, mRNA stability and PKC activity”)
  • BioCreative ResultsResults on Larger Dataset
  • A much larger test set has been made publicly available by Chiang and Yu.
  • EBI human test set
  • 4,410 genes
  • 13,626 GO code annotations
  • MGI mouse test set
  • 2,188 genes
  • 6,338 GO code annotations
  • Note that Chiang and Yu used the same data for both training and testing.
  • Results on EBI Human and MGI datasets
  • EBI human: 4,410 genes and 5,714 abstracts
  • MGI: 2,188 genes and 1,947 abstracts
  • Conclusions and Future Work
  • We propose an algorithm that annotates genes with GO codes using the information available from other species
  • Experimental results on three datasets show that our algorithm consistently achieves higher F-measures than other solutions
  • Future improvements to our algorithm: - combine or use a voting scheme between the predictions our system makes and the predictions of a machine learning system
  • - investigate how effective are other genes with sequences similar to the target gene (but not orthologous to the gene) for predicting the GO codesThank you!http://biotext.berkeley.eduResearch Supported by NSF DBI-0317510 and a gift from Genentech
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