This document summarizes information from several genomics and bioinformatics research groups and projects. It discusses: - The ENCODE project and its focus areas including databases, data mining, visualization, transcriptomics, alternative splicing, sequencing pipelines, comparative genomics, epigenomics, and population genomics. - Tools and databases for variant analysis from the 1000 Genomes Project and FORGE Consortium. - The Genome Modeling System from The Genome Institute at Washington University for analyzing TCGA, ICGC, 1000 Genomes, and PCGP data. - Using RNA-seq technology to reveal the transcriptome and methods for isolating translated mRNA. - Resources for analyzing Human Microbiome Project data

2. 163 Posters/46
talks/402
participants
poster
Other
Tal
http://www.gersteinlab.org/
ENCODE

4. 
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Databases Data Mining Visualization
and Curation.
Transcriptomics, Alternative Splicing
and Gene prediction.
Sequencing Pipeline and Assembly.
Comparative and Evolutionary
Genomics
Epigenomics and Non-coding Genome
Population and personal Genomics

5. 
Medsavant: integrated solution for storage,
annotation, filtration, prioritization and visual
inspection of variants. #1000 genome
project; #FORGE consortium 425 individuals
Mendel App
Finding of
Rare
disease
genes

6. 
The Genome Institute at Washington
University – Genome Modelling System.
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TCGA
ICGC (Int. Cancer Genome Consortium)
1000 genomes project
PCGP (Pediatric Cancer Genome Project)
https://github.com/genome/gms

7. 
RNA-seq (RNA Sequencing), also called
"Whole Transcriptome Shotgun
Sequencing" [1] ("WTSS"), is a technology that
uses the capabilities of next-generation
sequencing to reveal a snapshot of RNA
presence and quantity from a genome at a
given moment in time.[2]

8. 
Mario Stanke at Institute of Mathematics and
computer science, Germany
◦ Synteny based approach in gene prediction

Method for isolating Ribosome bound mRNA
◦ Tufts University, Boston, MA.
◦ Specifically can tell which mRNAs are actually
translated.

9. 
Data Analysis and coordination center
(DACC).
◦ For HMP data analysis
◦ CloVR virtual machine
◦ http://www.Hmpdacc.org/tools_protocols/tools_pr
otocols.php

In silico genome subtraction tool
◦ MGC (Microbial Genome Comparison tool) – A stand
alone Java package

10. 
Studying co-expression network with micrarrays and RNAseq
◦ 3320 microarray experiments vs 813 RNAseq
samples with 8 datasets
 Major biological difference
 Hundreds and thousands of samples necessary to
build a robust RNAseq dataset
 RNAseq co-expression analysis necessary.

11. 
RNAseq Analysis pipeline using Ion Torrent
data.
◦ Tophat-cufflink
◦ STAR-cufflink
◦ TMAP-cufflink
Expression levels are zero for STAR, TMAP aligners
•Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P,
Chaisson M, Gingeras TR: STAR: ultrafast universal RNA-seq aligner.
Bioinformatics 2013, 29:15-21. PubMed Abstract | Publisher Full Text
Return to text
•TMAP is a short read aligner specifically tuned for data from the Ion
Torrent PGM

12. •FPKM vs RSEM and cufflink
•Lior Pachter @lpachter1 Nov
@konrad_jk @tuuliel @joe_pickrell
@yarbsalocin I'm sure RSEM,
Sailfish, and many other tools can
handle it as well.
•http://liorpachter.wordpress.com
/seq/
RNAseq
Mortazavi, A., Williams,
B.A., McCue, K., Schaeffer,
L. & Wold, B. Mapping and
quantifying mammalian
transcriptomes by RNAseq. Nat. Methods 5, 621–
628 (2008).
ICEseq

13. 
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Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL,
Pachter L. Differential analysis of gene regulation at transcript
resolution with RNA-seq. Nat Biotechnol. 2013 Jan;31(1):46-53.
doi: 10.1038/nbt.2450. Epub 2012 Dec 9. PubMed PMID:
23222703.
Trapnell, C. et al. Transcript assembly and quantification by
RNA-seq reveals unannotated transcripts and isoform switching
during cell differentiation. Nat. Biotechnol. 28, 511–515(2010).
Dissects the difference between a gene and a transcript –
emphasizes on differential expression in isoform in a given
condition.
RPKM vs FPKM
(# of mapped reads)/(length of transcript in kilo base)/(million)
(# of fragments)/(length of transcript in kilo base)/(million)

14. •rsem-prepare-reference
•rsem-calculate-expression

15. SAILFISH
Sailfish: Alignment-free Isoform
Quantification from RNA-seq Reads using
Lightweight Algorithms
bio.math.berkeley.edu/eXpress/simdata
RSEM or
eXPRESS

16. 
A -> I editing ->
sequenced as Guanine
◦ Mapping to genome may
correct this, but how to
distinguish this from
SNP?


Adapted ICE (Inosine
Chemical Erasing)
using Cyanoethylation.
Computational
pipeline to analyze
this.

17. IICB features in Omics map…
www.omicsmaps.com

20. We created a virtual
machine…..
Akash Gupta
Arijit Panda
Subhadeep Das
Neha Sanghi
Madhu C.
Deeksha Singh
Arpita Ghorai
http://front.math.ucdavis.edu/
arXiv