Download Gene Networks and Metagenomics: Bioinformatics Algorithms and Tools - Prof. Mihai Pop and more Study notes Computer Science in PDF only on Docsity! CMSC423: Bioinformatic Algorithms, Databases and Tools Lecture 22 Gene networks Real-life examples Biological networks • Genes/proteins do not exist in isolation • Interactions between genes or proteins can be represented as graphs • Examples: – metabolic pathways – regulatory networks – protein-protein interactions (e.g. yeast 2-hybrid) – genetic interactions (synthetic lethality)
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Gene networks research at UMD • Active area of research in Carl Kingsford's lab • Data will be generated in Najib El Sayed's lab • My own research on microbial communities will translate into such data. Metagenomics Environment “exploration” • Culture-based – heavily biased (1-5% bacteria easily cultured) – amenable to many types of analyses • Directed rRNA sequencing – less biased – limited analyses possible • Random shotgun sequencing – “differently” biased – amenable to many types of analyses – $$$ Project overview • Collaboration between TIGR, Stanford, and Washington University (St. Louis) • Sequenced fecal samples from two healthy individuals (XX, XY) (veg+,veg-) correlation lost due to IRB • Also performed “traditional” amplified 16S rDNA sequencing 3,601 74,462 Subject 2 7,1153,514amplified 16S rDNA clones 139,52165,059Shotgun reads TotalSubject 1 All shotgun reads from ~ 2 kbp library Metagenomic pipeline • Assembly (graph theory, string matching) – puzzle-together shotgun reads into contigs and scaffolds • Gene finding (machine learning) • Binning (clustering, statistics) – assign each contig to a taxonomic unit • Annotation (natural language processing) – gene roles, pathways, orthologous groups, etc • Analysis (statistics, graph theory, data visualization) – diversity – comparison between environments – metabolic potential – etc. Metagenomics... • This work is ongoing at UMD with support from NSF and NIH • Paid summer internships available – contact me if you are interested. Assembly with optical maps Optical mapping data • Restriction mapping (set/bag of fragment sizes) – restriction digest – spectrum of sizes defines “fingerprint” • Optical mapping (list/array of fragment sizes) – ordered restriction digest – order of fragment sized defines fingerprint #. size (stdev) 1. 1.2 (0.3) 2. 4.1 (0.8) 3. 2.2 (0.5) ... Results – real data Yersinia kristensenii Optical map: 350 sites (AFLII) Assembly: 86 contigs, 404 sites 48 contigs have > 1 site 45 contigs can be placed 30 unique matches 15 placed by greedy 4.4Mb (93%) in scaffold Yersinia aldovae Optical map: 360 sites (AFLII) Assembly: 104 contigs, 411 sites 58 contigs have > 1 site 52 contigs can be placed 31 have unique matches 21 placed by greedy 3.7Mb (88%) in scaffoldUn-placed contigs appear to be mis-assemblies With Niranjan NagarajanNagarajan, Read, Pop. Bioinformatics 2008. Voxelation Voxelation • Brown, V.M., et al., High-throughput imaging of brain gene expression. Genome Res, 2002. 12(2): p. 244-54. • http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11827944 • Brown, V.M., et al., Multiplex three-dimensional brain gene expression mapping in a mouse model of Parkinson's disease. Genome Res, 2002. 12(6): p. 868-84. • http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12045141 • Gene expression information in a spatial context • Combines microarray analysis with computer graphics Vanessa M. Brown et al. Genome Res. 2002; 12: 868-884 Figure 7 SVD delineates anatomical regions of the brain Vanessa M. Brown et al. Genome Res. 2002; 12: 868-884 Figure 5 Putative regulatory elements shared between groups of correlated and anticorrelated genes Vanessa M. Brown et al. Genome Res. 2002; 12: 868-884 Figure 6 Differentially expressed genes
