unknown genes, Community Profiling,& Biotorrents.netMorgan Langille UC Davis

Genes with unknown function

QuestionsIf we wanted to start studying a gene of unknown function, which one(s) should we study first?How many un-annotated genes could be annotated?What proportion of unknown genes (hypothetical proteins) are probably not real proteins (i.e. pseudo-genes, mis-predicted orfs, etc.) ?What proportion of unknown gene families are probably phage-related?Can some of these families (hopefully the top ranking ones) be characterized using non-similarity based bioinformatic approaches?

Outline of project

Community Profiling

Phylogenetic profilingWu, et al., PLOS Genetics, 2005C. hydrogenoformansidentified presence or absence of homologs in all other completely sequence genomesIdentified many hypothetical proteins that had the same profile as other sporulation proteins

Community ProfilingKEGGCOGDelong, et al., Science, 2006

Community ProfilingLook across multiple metagenomic samplesGene families that have similar profiles may have similar functionSimilar to using co-expression to identify similar functioning genes

So what have I done? "all metagenomics peptides" from CAMERA 43M sequences (mostly GOS)Searched against 11,000 Pfams using HMMER 3Used “cluster” to group genes and samples

ResultsMetagenomic SamplesRed = above avg. number of pfamsGreen = below avg. number of pfamsHave not normalizedNumber of sequences per sampleFor number of pfamsPfams

Example of phage Pfams clustering together

Measuring functional relatedness Need to measure community profiling performanceThe hierarchal clusters were broken into 575 groups using a correlation cutoff of 0.90 or above. PFams were mapped to GO terms using pfam2GO1893 PFams had no associated GO term 695 of these were Domains of Unknown Function:DUFs3377 PFams had one or more associated GO terms and could be used for further analysis Only 67 (of 575) clusters contained 4 or more PFams with at least one GO term

Measuring GO similarityG-SESAME Measures the semantic similarity of any two GO termsNot downloadable so queries had to be made to their web server (not fun)Pair-wise similarity was measure for each pair of GO terms in each cluster had to check if terms were in same namespace

ResultsAverage G-Sesame scores for each clusterThe average of all cluster averages was 0.484 10 clusters had a score of 0.60 or greater. The data was then randomized by using the same GO terms but in different random clusters and a score of 0.412-0.420 over 4 iterations Each of the 4 iterations had only 1 or 0 clusters with a score of 0.60 or greater

Community Profiling Results Average of all clusters= 0.49 10 clusters are > 0.60

Average of all clusters= 0.49

10 clusters are > 0.60

Random Results Average of all clusters (4 iterations) = 0.41 - 0.42 1 or 0 clusters are > 0.60

Average of all clusters (4 iterations) = 0.41 - 0.42

1 or 0 clusters are > 0.60

BioTorrents

BittorrentA peer-to-peer file sharing protocol~ 27-55% of all Internet trafficMostly illegal file sharingFiles are shared in small pieces between several users

Torrents for BiologyWhy use torrent technology?Download large datasets much fasterSearchable central listingDecentralization of data

What is BioTorrents?A legal file sharing website for scientistsUsers can upload their own research results, data, softwareUsers can browse or search through all datasetsData is not hosted on BioTorrents

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Who will upload data?Everyone! Realistically,Large organizations (e.g. NCBI, CAMERA, etc.) May need some convincing to host their data via torrents in addition to FTP, HTTP, etc. Scientists that really support open science Sharing data before formally complete and published

Technical Challenges Many institutions frown on BitTorrent technologyA port must be opened/forwardedClient program and computer must be left runningEnsuring data is legal, virus free, etc.Users that upload many legitimate torrents will provide more confidence to people downloadingMaking downloading and uploading easy