Orthologs Detection and Applications
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1 Orthologs Detection and Applications Marcus Lechner Bioinformatics Leipzig Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
2 Table of contents 1 Background on homology 2 Proteinortho 3 Domain wide commons 4 Annotation pipeline 5 References Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
3 Definitions Homologous genes have derived from a common ancestor Orthology evolved by speciation thought to have a similar function Paralogy homologous genes within the same species thought to have a related function (neo-/subfunctionalization) out-paralogs arose form a duplication preceding a speciation in-paralogs evolved by duplication subsequent to speciation Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
4 Example Figure: Illustration of relationships: Three species with orthologs, xeno-, in- and out-paralogs Adapted from [1] Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
5 Problems Interpretation original definition of homology (1843): the same organ under every variety of form and function [2] Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
6 Problems Interpretation original definition of homology (1843): the same organ under every variety of form and function [2] still a very good quantitative indication but neither essential nor sufficient Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
7 Problems Interpretation original definition of homology (1843): the same organ under every variety of form and function [2] still a very good quantitative indication but neither essential nor sufficient Homology of two proteins is not equivalent with a common function, sequence nor structure! Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
8 Problems Relative definition in-/out-paralog definition only in subjection to a certain species greatly dependent on available data no absolute view Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
9 Problems Figure: Illustration of relationships: Complete view needed Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
10 Problems Figure: Illustration of relationships: Complete view needed Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
11 Problems Information benefit duplications are known to be a major source of innovation in evolution proteins are homologs per definition, if they have a common ancestor irrespective of their actual similarity or function most proteins are anciently related but have evolved far Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
12 Problems Figure: Multiple gene duplications: All are homologs per definition but smaller groups may be more of use Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
13 Problems Information benefit duplications are known to be a major source of innovation in evolution proteins are homologs per definition, if they have a common ancestor irrespective of their actual similarity or function most proteins are anciently related but have evolved far Up to which point is the homology information useful? Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
14 Conclusion Proteinortho approach arose from the same ancestor + similar function similar sequence should return a useful subset of homologs (isofunctional aimed) reciprocal best blast(s) Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
15 Reciprocal best blast(s) for homologs detection Figure: Homology detection using blast Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
16 Proteinortho Features orthologs and paralogs assignment for proteins/protein coding genes designed for large-scale application behaves nicely in memory consumption capable of distributed computing Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
17 Workflow Figure: Proteinortho workflow: 1) Reciprocal blasts 2) Transformation into graph representation 3) Coloring and decomposition 4) Reconversion and mapping to species with encoded proteins Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
18 Distributed computing Figure: a) Multiple PCs running Proteinortho, cooperating dynamically using an N-way technique b) Workflow of synchronization Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
19 Challenge Application to all bacteria available on NCBI 710 species, 15 million proteins Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
20 Challenge Application to all bacteria available on NCBI 710 species, 15 million proteins took about two weeks on 50 CPU-cores (Intel Xenon 233 GHz) peak of only 25 GB RAM, but 300 GB hard disk Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
21 Results 300 Coverage overview cumulative # of connected components original blasted blasted filtered # of species covered Figure: Number of common proteins Sets with over 5% paralogs where filtered Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
22 Results Common proteins 30S ribosomal proteins S2-5, S7, S8, S10-13, S17, S19 50S ribosomal proteins L1-3, L5, L6, L11, L14, L22, L23 trna synthetases for seryl, arginyl, phenylalanyl (alpha chain) preprotein translocase, SecY subunit peptidase M22, O-sialoglycoprotein endopeptidase transcription elongation/termination factor NusA Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
23 Annotation pipeline Application for annotation in: newly sequenced bacterial genome out: annotation of protein coding genes candidates for non-coding genes no previous knowledge required runs in 10 to 90 minutes Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
24 Relatives discovery Figure: Relatives detection using reference proteins and tree Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
25 Relatives discovery with colors Figure: Advanced relatives detection using colors Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
26 Seeding Figure: Pipeline seeding with proteins Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
27 Pipeline overview Figure: Pipline seeding with proteins Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
28 The end Thank you for listening! Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
29 W M Fitch Homology a personal view on some of the problems Trends Genet, 16(5):227 31, May 2000 Richard Owen, Cooper, and William White Lectures on the comparative anatomy and physiology of the invertebrate animals London :Longman, Brown, Green, and Longmans, Marcus Lechner (Bioinformatics Leipzig) Orthologs Detection and Applications / 25
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