BOL: Related items

The genome factory !!!

Madhvan Reddy — Thu, 16 Jan 2014 02:09:31 -0600

Illumina, Inc. announced Tuesday that its new HiSeq X Ten Sequencing System has broken the “sound barrier” of human genomics by enabling the $1,000 genome. “This platform includes dramatic technology breakthroughs that enable researchers to undertake studies of unprecedented scale by providing the throughput to sequence tens of thousands of human whole genomes in a single year in a single lab,” Illumina stated.

Initial customers for the HiSeq X Ten System, which will ship in Q1 2014, include Macrogen, based in Seoul, South Korea and its CLIA laboratory in Rockville, Maryland, the Broad Institute in Cambridge, Massachusetts, and the Garvan Institute of Medical Research in Sydney, Australia.

“For the first time, it looks like it will be possible to deliver the $1,000 genome, which is tremendously exciting,” said Eric Lander, founding director of the Broad Institute and a professor of biology at MIT. “The HiSeq X Ten should give us the ability to analyze complete genomic information from huge sample populations. Over the next few years, we have an opportunity to learn as much about the genetics of human disease as we have learned in the history of medicine.”

“The HiSeq X Ten is an ideal platform for scientists and institutions focused on the discovery of genotypic variation to enable a deeper understanding of human biology and genetic disease,” Illumina stated. “It can sequence tens of thousands of samples annually with high-quality, high-coverage sequencing, delivering a comprehensive catalog of human variation within and outside coding regions.”

HiSeq X Ten utilizes a number of advanced design features to generate massive throughput. Patterned flow cells, which contain billions of nanowells at fixed locations, combined with a new clustering chemistry deliver a significant increase in data density (6 billion clusters per run). Using state-of-the art optics and faster chemistry, HiSeq X Ten can process sequencing flow cells more quickly than ever before — generating a 10x increase in daily throughput when compared to current HiSeq 2500 performance.

The HiSeq X Ten is sold as a set of 10 or more ultra-high throughput sequencing systems, each generating up to 1.8 terabases (Tb) of sequencing data in less than three days or up to 600 gigabases (Gb) per day, per system, providing the throughput to sequence tens of thousands of high-quality, high-coverage genomes per year. Illumina says the $1,000 includes typical instrument depreciation, DNA extraction, library preparation, and estimated labor.

Introduction to Bioinformatics

eliabrodsky — Wed, 05 Jun 2019 14:58:11 -0500

Introduction to bioinformatics is a course for biologists and clinicians that would like to learn more about the way bioinformatics is used in healthcare, biotech and pharmaceuitcal industry as well as basic research. The course covers many of the topics transformed by the emergence of big data and computational technologies. To learn more about the course, visit: https://edu.t-bio.info/course/introduction-bioinformatics/

Next generation sequencing in R or bioconductor environment

John Parker — Mon, 02 Jun 2014 18:03:09 -0500

There are many R software and bioconductor packages for NGS data analysis, some of them are as follows

Biostrings

The Biostrings package from Bioconductor provides an advanced environment for efficient sequence management and analysis in R. It contains many speed and memory effective string containers, string matching algorithms, and other utilities, for fast manipulation of large sets of biological sequences. The objects and functions provided by Biostrings form the basis for many other sequence analysis packages. Documentation

IRanges Overview

IRanges provides the low-level infrastructure and containers for handling sets of integer ranges within Bioconductor's BioC-Seq domain. Its classes and methods provide support for many more high-level packages like GenomicRanges, ShortRead, Rsamtools, etc. Documentation

GenomicRanges Overview

The GenomicRanges package serves as the foundation for representing genomic locations within the Bioconductor project. It is built upon the IRanges infrastructure and defines three major data containers - GRanges, GRangesList and GappedAlignments - which are supporting other important BioC-Seq packages including ShortRead, Rsamtools, rtracklayer, GenomicFeatures and BSgenome. Compared to the IRanges container, the GRanges/GRangesList classes are more flexible and extensible to store additional information about sequence ranges, such as chromosome identifiers (sequence space), strand information and annotation data. Documentation

Motif Discovery

cosmo

The cosmo package allows to search a set of unaligned DNA sequences for a shared motif that may function as transcription factor binding site. The algorithm extends the popular motif discovery tool MEME (Bailey and Elkan, 1995) in that it allows the search to be supervised by specifying a set of constraints that the motif to be discovered must satisfy. Documentation

BCRANK

BCRANK is a method that takes a ranked list of genomic regions as input and outputs short DNA sequences that are overrepresented in some part of the list. The algorithm was developed for detecting transcription factor (TF) binding sites in a large number of enriched regions from high-throughput ChIP-chip or ChIP-seq experiments, but it can be applied to any ranked list of DNA sequences. Documentation

rGADEM: Documentation

MotIV: Documentation

ShortRead

The ShortRead package provides input, quality control, filtering, parsing, and manipulation functionality for short read sequences produced by high throughput sequencing technologies. While support is provided for many sequencing technologies, this package is primairly focused on Solexa/Illumina reads. Documentation

Rsamtools

Rsamtools provides functions for parsing and inspecting samtools BAM formatted binary alignment data. SAM/BAM is quickly becoming a universal standard alignment format, and is now supported by a wide variety of alignment tools. Documentation

Samtools Website
BWA (Burrows-Wheeler Alignment) Website

Additional tools for SNP analysis:

snpMatrix

BSgenome

BSgenome provides an object oriented infrastructure for interacting with a Biostring based genome sequence. BSgenome packages exist for many common genomes, and can be created to represent custom genomes. See the "How to forge a BSgenome data package" Vignette for instructions to create a new BSgenome package if a prebuilt package does not exist for your organism. Documentation

rtracklayer

rtracklayer provides an interface for exporting annotation feature data to various genome browsers and file formats (such as GFF). See the Small RNA Profiling exercise for an example of using rtracklayer to visualize alignment coverage. Documentation

biomaRt

The biomaRt package, provides an interface to a growing collection of databases implementing the BioMart software suite (http:// www.biomart.org). The package enables online retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas. This data is retrieved automatically via the Internet, so it's recommended that you cache the data locally, or check versions if your code will be adversely affected by updates to these data. Documentation

ChIP-Seq Analysis Packages

Bioconductor provides various packages for analyzing and visualizing ChIP-Seq data. Only a small selection of these packages is introduced here. Additional useful introductions to this topic are: BioC ChIP-seq Case Study and BioC ChIP-Seq.

chipseq

The chipseq package combines a variety of HT-Seq packages to a pipeline for ChIP-Seq data analysis. Documentation

BayesPeak

BayesPeak is a peak calling package for identifying DNA binding sites of proteins in ChIP-Seq experiments. Its algorithm uses hidden Markov models (HMM) and Bayesian statistical methods. The following sample code introduces the identification of peaks with the BayesPeak package as well as the incorporation of read coverage information obtained by the chipseq package. Documentation [ Publication ]

PICS

The PICS package applies probabilistic inference to aligned-read ChIP-Seq data in order to identify regions bound by transcription factors. PICS identifies enriched regions by modeling local concentrations of directional reads, and uses DNA fragment length prior information to discriminate closely adjacent binding events via a Bayesian hierarchical t-mixture model. The following sample code uses the test data set from the above BayesPeak package in order to compare the results from both methods by identifying their consensus peak set. Documentation [ Publication ]

ChIPpeakAnno

The ChIPpeakAnno package provides. batch annotation of the peaks identified from either ChIP-seq or ChIP-chip experiments. It includes functions to retrieve the sequences around peaks, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. The package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. Documentation

Additional ChIP-Seq Packages

DiffBind: Documentation

MOSAICS: Documentation

iSeq: Documentation

ChIPseqR: Documentation

ChiPsim: Documentation

CSAR: Documentation

ChIP-Seq Pipeline: PICS, rGADEM and MotIV (developer web site)

SPP: ChIP-seq processing pipeline

SPP Tutorial

MACS

SIPeS

RNA-Seq Analysis

Counting Reads that Overlap with Annotation Ranges

The GenomicRanges package provides support for importing into R short read alignment data in BAM format (via Rsamtools) and associating them with genomic feature ranges, such as exons or genes. This way one can quantify the number of reads aligning to annotated genomic regions. The package defines general purpose containers for storing genomic intervals as well as more specialized containers for storing alignments against a reference genome. The two main functions for read counting provided by this infrastructure are countOverlaps and summarizeOverlaps. For their proper usage, it is important to read the corresponding PDF manual. Documentation

Differential Gene Expression Analysis with DESeq

The DESeq package contains functions to call differentially expressed genes (DEGs) in count tables based on a model using the negative binomial distribution. It expects as input a data frame with the raw read counts per region/gene of interest (rows) for each test sample (columns). Such a count table can be imported into R or generated from BAM alignment files using the countOverlaps function as introduced above. Documentation

Differential Gene Expression Analysis with edgeR

The edgeR package uses empirical Bayes estimation and exact tests based on the negative binomial distribution to call differentially expressed genes (DEGs) in count data.

Documentation

A variety of additional R packages are available for normalizing RNA-Seq read count data and identifying differentially expressed genes (DEG):

easyRNASeq (simplifies read counting per genome feature)

DEXSeq (Inference of differential exon usage); parathyroidSE explains how to generate exon read counts in R

DEGseq

baySeq (also see: segmentSeq)

Genominator (Bullard et al. 2010)

Detection of Alternative Splice Junctions

Another utility of RNA-Seq experiments is the analysis of splice junctions. The following software suggestions provide this utility:

ERANGE
TopHat

SpliceMap

SplitSeek

DNA-Methylation Data Analysis

methylPipe
bsseq
BiSeq
Much more under BiocViews

HT-Seq Data Visualization

ggbio: ggplot2 extension for genomics data (online manual) Gviz: Plotting data and annotation information along genomic coordinates HilbertVis: Hilbert genome plots

GenomeGraphs: Plotting genomic information from Ensembl

TileQC: Flow Cell Quality Visualization

rtracklayer: R interface to genome browsers

genoPlotR: Plotting maps of genes and genomes

Genominator: Tools for storing, accessing, analyzing and visualizing genomic data.

To install all packages

source("http://bioconductor.org/biocLite.R")
biocLite()
biocLite(c("ShortRead", "Biostrings", "IRanges", "BSgenome", "rtracklayer", "biomaRt", "chipseq", "ChIPpeakAnno", "Rsamtools", "BayesPeak", "PICS", "GenomicRanges", "DESeq", "edgeR", "leeBamViews", "GenomicFeatures", "BSgenome.Celegans.UCSC.ce2"))

Bioinformatics algorithms tutorials

John Parker — Tue, 24 Jun 2014 00:10:45 -0500

Useful bioinformatics tutorial, such as

De Bruijn Graphs for NGS Assembly
Algorithms for PacBio Reads
Software and Hardware Concepts for Bioinformatics
Finding us in Homolog.us (Search Algorithms)
NGS Genome and RNAseq Assembly - a Hands on Primer
Introduction to PERL, Python, R and C/C++ for Bioinformatics

Address of the bookmark: http://www.homolog.us/Tutorials/

Orione – a web-based framework for NGS analysis in microbiology

Martin Jones — Wed, 23 Jul 2014 06:43:03 -0500

End-to-end NGS microbiology data analysis requires a diversity of tools covering bacterial resequencing, de novo assembly, scaffolding, bacterial RNA-Seq, gene annotation and metagenomics. However, the construction of computational pipelines that use different software packages is difficult due to a lack of interoperability, reproducibility, and transparency. To overcome these limitations researchers at CRS4, Italy have developed Orione, a Galaxy-based framework consisting of publicly available research software and specifically designed pipelines to build complex, reproducible workflows for NGS microbiology data analysis. Enabling microbiology researchers to conduct their own custom analysis and data manipulation without software installation or programming, Orione provides new opportunities for data-intensive computational analyses in microbiology and metagenomics.

Reference

Cuccuru G1, Orsini M, Pinna A, Sbardellati A, Soranzo N, Travaglione A, Uva P, Zanetti G, Fotia G. (2014) Orione, a web-based framework for NGS analysis in microbiology. Bioinformatics [Epub ahead of print]. [article]

Address of the bookmark: http://orione.crs4.it/

PostDoc Scientist Bioinformatics at CCMB

Fri, 26 Sep 2014 19:58:41 -0500

1. Project Assistant/Junior Research Fellow/ Project Fellow [PA_JRF_PF]

a) M.Sc/or equivalent in biological sciences/related areas [Position Code: PA_JRF_PF_a]
b) B.E/B.Tech/ M.Sc in biotechnology/bioinformatics/computer science/Chemistry/Physics or MCA [Position Code: PA_JRF_PF_b]
c) M.Sc/or equivalent in wildlife sciences/ecology/environmental sciences or MBBS/BVSc/MVSc. [Position Code: PA_JRF_PF_c]

(Candidates with result awaited are NOT eligible to apply)

Upper Age limit 28years

Rs.12000 / Rs.16000 (as sanctioned by the funding agency)

2. Post Doctoral Fellow/Research Associate in multiple research areas [PDF_RA]

Ph.D. (submitted/awarded) in any branch of biological Sciences. Candidates with Ph.D. in other sciences are also encouraged to apply.

Experience in molecular biology, biochemistry, structural biology, cell biology, infectious disease, conservation genetics, veterinary science, reproductive biology, and molecular diagnostics is desired but not mandatory.

[Position Code: PDF_RA]

UpperAge limit 35years

Rs. 22000- 26000 (as sanctioned by the funding agency)

3. Post Doctoral Scientist Fellow [PDSF]

Ph.D in any of the following areas: bioinformatics, next generation sequencing, high throughput data analysis, proteomics, bio-statistics, computer science, information technology, computer hardware and networking/clustering, parallel processing.
[Position Code: PDSF]

Upper Age limit 40 years

Rs. 40000 consolidated (as sanctioned by the funding agency)

Download Application: Last date for apply online: 09th Oct 2014

Advertisement: www.ccmb.res.in//index.php?view=notifications&mid=0&id=71&nid=38

Apply online http://www.ccmb.res.in/positions/temp_notif/online_form.html

More at http://www.ccmb.res.in//index.php?view=notifications&mid=0&id=71&nid=38

ArrayGen Bioinformatics Genomics Group

Sun, 28 Sep 2014 14:09:55 -0500

ArrayGen is a global bioinformatics company which is a one stop solution for microarray designing and genomics data analysis. Our novel Array Design Approach Strategy (ADAS) aims to condense the time lag between demands of scientific community and manufacture industry, thereby expediting research processes.

ArrayGen specializes in Genomics data analysis and research, as we believe in the level of precision, predictability, benchmark-ability, and data analysis capability of genomics data over other forms of biological data. ArrayGen constantly strives to develop new solutions, and plug the existing gaps in the technological advancement of the field.

More http://www.arraygen.com/

Postdoc position in protein annotation and machine learning - Paris, France

Sat, 04 Oct 2014 08:10:45 -0500

We are interested in finding an excellent postdoc with interests in protein functional annotation, machine learning and computer grids. The position is open for 3.5 years at the Université Pierre et Marie Curie, in the heart of Paris.

Research topic: Protein function annotation, multiple probabilistic models, domain architecture, machine learning, combinatorial optimization, computer grid.

This project is run on the Laboratoire de Biologie Computationnelle et Quantitative UMR7238 CNRS-UPMC – Analytical Genomics team, headed by A.Carbone. It is co-advised with Pierre-Henri Wuillemin, Laboratoire d’Informatique de Paris 6 – Equipe DECISION.

The postdoc will be payed under a contract of Ingénieur de Recherche lasting 3.5 years and it is available from September 1st, 2014.

Group Web Page: http://www.lcqb.upmc.fr/AnalGenom/home.html

Ref. E-Mail: Alessandra Carbone alessandra.carbone@lip6.fr

Ryan E. Mills Lab

Tue, 26 May 2015 09:29:24 -0500

Our research group is primarily focused on the analysis of whole genome sequence data to identify genetic variation (primarily structural variation) and examine their potential functional impact in disease phenotypes. We are particularly interested in analyzing complex regions of the genome that are not easily resolved through modern sequencing approaches and which may exhibit interesting mechanistic origins.

We are also interested in the large-scale integration of genomic, expression, methylation and proteomic data sets, as well as the application of whole genome sequence analysis in clinical diagnostics.

More at http://millslab.ccmb.med.umich.edu/index.html

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/