BOL: Related items

Biinformamatics Lead at Google Life Sciences

Fri, 17 Oct 2014 02:24:55 -0500

Google Life Sciences is recruiting a technical lead with experience in bioinformatics and clinical bioinformatics, including for biomarker discovery projects such as the Baseline study.

Responsibilities

Lead teams of scientists in structuring, prototyping, and executing large-scale bioinformatic and other analysis.
Develop novel bioinformatics, statistical, data processing, pathway, data mining and other algorithms to identify biological signals and their clinical correlates in broad kinds of individual and population data.
Develop novel platform-level analytical tools for sequence-based assays (assembly, annotation, variant calling and interpretation, phasing, genome structure, etc.), expression assays (RNAseq and microarray), proteomics, and metabolomics.
Develop statistical models that robustly correlate complex laboratory-derived information with phenotypic and clinical information.
Create scientifically rigorous visualizations, communications, and presentations of results.

Reference @ https://www.google.com/about/careers/search#!t=jo&jid=62095001

Zhang Lab

Sun, 28 Dec 2014 12:43:08 -0600

We develop and use integrative bioinformatics approaches to extract biological meanings from experimental data and generate hypotheses for experimental validation. Please explore our website to learn more about our people and our research.

More at http://bioinfo.vanderbilt.edu/zhanglab/

Raphael Lab

Sat, 04 Jul 2015 19:05:29 -0500

Raphael Lab research is focused on Bioinformatics and Computational Biology.

Current research interests include next-generation DNA sequencing, structural variation, genome rearrangements in cancer and evolution, and network analysis of somatic mutations in cancer. Earlier research included topics in comparative genomics, multiple sequence alignment, and motif finding.

More athttp://compbio.cs.brown.edu/

Will MinION Nanopore sequencing increase the number of Next Generation Sequencing projects?

Strand — Tue, 04 Aug 2015 05:14:07 -0500

Will MinION Nanopore sequencing increase the number of Next Generation Sequencing projects?

n50PlottingTools

Jit — Mon, 08 Feb 2016 15:39:04 -0600

Tools to create plots showing N-statistics for genome assemblies

More at https://github.com/dentearl/n50PlottingTools

Address of the bookmark: https://github.com/dentearl/n50PlottingTools

Stacks

Jitendra Narayan — Wed, 24 Feb 2016 15:52:30 -0600

Stacks is a software pipeline for building loci from short-read sequences, such as those generated on the Illumina platform. Stacks was developed to work with restriction enzyme-based data, such as RAD-seq, for the purpose of building genetic maps and conducting population genomics and phylogeography.

More at http://catchenlab.life.illinois.edu/stacks/

Address of the bookmark: http://catchenlab.life.illinois.edu/stacks/

RNA-Seq De novo Assembly Using Trinity

Surabhi Chaudhary — Wed, 23 Mar 2016 05:53:46 -0500

Trinity, developed at the Broad Institute and the Hebrew University of Jerusalem, represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes. Briefly, the process works like so:

Inchworm assembles the RNA-seq data into the unique sequences of transcripts, often generating full-length transcripts for a dominant isoform, but then reports just the unique portions of alternatively spliced transcripts.
Chrysalis clusters the Inchworm contigs into clusters and constructs complete de Bruijn graphs for each cluster. Each cluster represents the full transcriptonal complexity for a given gene (or sets of genes that share sequences in common). Chrysalis then partitions the full read set among these disjoint graphs.
Butterfly then processes the individual graphs in parallel, tracing the paths that reads and pairs of reads take within the graph, ultimately reporting full-length transcripts for alternatively spliced isoforms, and teasing apart transcripts that corresponds to paralogous genes.

More at https://github.com/trinityrnaseq/trinityrnaseq/wiki

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Download Trinity here.

Build Trinity by typing 'make' in the base installation directory.

Assemble RNA-Seq data like so:

 Trinity --seqType fq --left reads_1.fq --right reads_2.fq --CPU 6 --max_memory 20G

Find assembled transcripts as: 'trinity_out_dir/Trinity.fasta'

Address of the bookmark: https://github.com/trinityrnaseq/trinityrnaseq/wiki

Sequence assembly with MIRA 4

Priya Singh — Wed, 06 Apr 2016 08:21:22 -0500

MIRA is a multi-pass DNA sequence data assembler/mapper for whole genome and EST/RNASeq projects. MIRA assembles/maps reads gained by

electrophoresis sequencing (aka Sanger sequencing)
454 pyro-sequencing (GS20, FLX or Titanium)
Ion Torrent
Solexa (Illumina) sequencing
(in development) Pacific Biosciences sequencing

into contiguous sequences (called contigs). One can use the sequences of different sequencing technologies either in a single assembly run (a true hybrid assembly) or by mapping one type of data to an assembly of other sequencing type (a semi-hybrid assembly (or mapping)) or by mapping a data against consensus sequences of other assemblies (a simple mapping).

The MIRA acronym stands for Mimicking Intelligent Read Assembly and the program pretty well does what its acronym says (well, most of the time anyway). It is the Swiss army knife of sequence assembly that I've used and developed during the past 14 years to get assembly jobs I work on done efficiently - and especially accurately. That is, without me actually putting too much manual work into it.

More at http://mira-assembler.sourceforge.net/docs/DefinitiveGuideToMIRA.html

Address of the bookmark: http://mira-assembler.sourceforge.net/docs/DefinitiveGuideToMIRA.html

Trimmomatic: A flexible read trimming tool for Illumina NGS data

Jit — Fri, 15 Apr 2016 05:58:53 -0500

Paired End:

java -jar trimmomatic-0.35.jar PE -phred33 input_forward.fq.gz input_reverse.fq.gz output_forward_paired.fq.gz output_forward_unpaired.fq.gz output_reverse_paired.fq.gz output_reverse_unpaired.fq.gz ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36

This will perform the following:

Remove adapters (ILLUMINACLIP:TruSeq3-PE.fa:2:30:10)
Remove leading low quality or N bases (below quality 3) (LEADING:3)
Remove trailing low quality or N bases (below quality 3) (TRAILING:3)
Scan the read with a 4-base wide sliding window, cutting when the average quality per base drops below 15 (SLIDINGWINDOW:4:15)
Drop reads below the 36 bases long (MINLEN:36)

More at http://www.usadellab.org/cms/?page=trimmomatic

Address of the bookmark: http://www.usadellab.org/cms/?page=trimmomatic

ALE: a Generic Assembly Likelihood Evaluation Framework for Assessing the Accuracy of Genome and Metagenome Assemblies

Neel — Tue, 26 Apr 2016 03:38:43 -0500

Assembly Likelihood Evaluation (ALE) framework that overcomes these limitations, systematically evaluating the accuracy of an assembly in a reference-independent manner using rigorous statistical methods. This framework is comprehensive, and integrates read quality, mate pair orientation and insert length (for paired-end reads), sequencing coverage, read alignment and k-mer frequency. ALE pinpoints synthetic errors in both single and metagenomic assemblies, including single-base errors, insertions/deletions, genome rearrangements and chimeric assemblies presented in metagenomes. At the genome level with real-world data, ALE identifies three large misassemblies from the Spirochaeta smaragdinae finished genome, which were all independently validated by Pacific Biosciences sequencing. At the single-base level with Illumina data, ALE recovers 215 of 222 (97%) single nucleotide variants in a training set from a GC-rich Rhodobacter sphaeroides genome. Using real Pacific Biosciences data, ALE identifies 12 of 12 synthetic errors in a Lambda Phage genome, surpassing even Pacific Biosciences' own variant caller, EviCons. In summary, the ALE framework provides a comprehensive, reference-independent and statistically rigorous measure of single genome and metagenome assembly accuracy, which can be used to identify misassemblies or to optimize the assembly process.

More at http://www.ncbi.nlm.nih.gov/pubmed/23303509

Address of the bookmark: http://sc932.github.io/ALE/about.html