BOL: Related items

BioRG

Tue, 04 Aug 2015 20:52:52 -0500

This research group works on problems from the fields of Bioinformatics, Biotechnology, Data Mining, and Information Retrieval. The group's research projects includes Comparative Genomics of Bacterial genomes, Metagenomics, Genomic databases, Pattern Discovery in sequences and structures, micro-array data analysis, prediction of regulatory elements, primer design, probe design, phylogenetic analysis, medical image processing, image analysis, data integration, data mining, information retrieval, knowledge discovery in electronic medical records, and more.

More at http://biorg.cis.fiu.edu/

Rajiv Gandhi Centre for Biotechnology (RGCB) invites applications for the following three faculty scientist

Tue, 24 Nov 2015 22:13:16 -0600

Scientist Positions
Advt. No.RGCB Advt./SCI 2015/1

November 11, 2015

Rajiv Gandhi Centre for Biotechnology (RGCB) invites applications for the following three faculty scientist positions:

Scientist E-II or F in Bioinformatics & Computational Biology

SCIENTIST E-II OR F IN COMPUTATIONAL BIOLOGY & BIOINFORMATICS

Highly motivated and innovative individual who will pursue basic research, solve biological problems with emphasis on computational and quantitative experimental methods and build active bridges to translational research. The scientist will also provide computational biology support to ongoing research programs in disease biology, provide assistance to analyze complex data sets generated by RGCB scientists and collaborators inclusive of including high dimensional “omics” data and next generation sequencing data, such as whole genome, exome, RNA-seq and ChIP-seq as well as provide leadership for high quality training for junior scientists and regular teaching programs of the institute. Areas of research of interest to RGCB include but are not limited to computational, systems, or quantitative biology with applications to cell biology, developmental biology, metabolism, genomics, proteomics, biophysics, biological information systems, network pharmacology, drug design and cancer research. The scientist’s responsibilities include efforts for the integration of DNA variant annotation with statistical genetic analysis methods including linkage, imputation and association methods, adopting novel and innovative methodologies to analyze, integrate and interpret high dimensional data sets, provision of annotation to robust genetics and genomics findings using several data sources and methods, data management of exploratory clinical and R&D studies in partnership with other lines of genetic data generated from internal and external studies, delivery and documentation of genomic information to support genetic studies, ensuring high-quality genetic and genomic data is incorporated into exploratory- clinical research programs, developing tools that make maximum use of multiple data sources to support annotation of DNA variation and contributes to systems biology initiatives within RGCB

More at http://rgcb.res.in/scientist-positions/

Application Form http://rgcb.res.in/wp-content/uploads/2015/11/APPLICATION-FORMAT-FOR-SCIENTISTS.docx

MAKER

Jitendra Narayan — Sun, 07 Feb 2016 15:59:24 -0600

MAKER is a portable and easily configurable genome annotation pipeline.Its purpose is to allow smaller eukaryotic and prokaryotic genome projects to independently annotate their genomes and to create genome databases. MAKER identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values.

More at http://www.yandell-lab.org/software/maker.html

Address of the bookmark: http://www.yandell-lab.org/software/maker.html

liftover

Jitendra Narayan — Mon, 08 Feb 2016 15:45:03 -0600

Convenient conversions between genome assemblie. The liftover package makes it easy to remap genomic coordinates to a different genome assembly.

More at https://github.com/aaronwolen/liftover

https://www.bioconductor.org/help/workflows/liftOver/

Address of the bookmark: https://github.com/aaronwolen/liftover

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

SPAdes

Abhimanyu Singh — Tue, 19 Apr 2016 08:37:08 -0500

SPAdes – St. Petersburg genome assembler – is intended for both standard isolates and single-cell MDA bacteria assemblies. This manual will help you to install and run SPAdes. SPAdes version 3.7.1 was released under GPLv2 on March 8, 2016 and can be downloaded from http://bioinf.spbau.ru/en/spades.

Manual at http://spades.bioinf.spbau.ru/release3.7.1/manual.html

Address of the bookmark: http://bioinf.spbau.ru/spades

BUSCO: Assessing genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs

Anjana — Tue, 10 May 2016 07:46:24 -0500

High-throughput genomics has revolutionized biological research, however, while the number of sequenced genomes grows by the day, quality assessment of the resulting assembled sequences remains complicated and mostly limited to technical measures like N50.
BUSCO provides measures for quantitative assessment of genome assembly, gene set, and transcriptome completeness based on evolutionarily informed expectations of gene content from near-universal single-copy orthologs selected from OrthoDB.
BUSCO assessments are implemented in open-source software, with comprehensive lineage-specific sets of Benchmarking Universal Single-Copy Orthologs for arthropods, vertebrates, metazoans, fungi, eukaryotes, and bacteria.
These conserved orthologs are ideal candidates for large-scale phylogenomics studies, and the annotated BUSCO gene models built during genome assessments provide a comprehensive gene predictor training set for use as part of genome annotation pipelines.
BUSCO assessments offer intuitive metrics, based on evolutionarily informed expectations of gene content from hundreds of species, to gauge completeness of rapidly accumulating genomic data and satisfy an Iberian's quest for quality - "Busco calidad/qualidade".

Address of the bookmark: http://busco.ezlab.org/

AccNET

Jitendra Narayan — Fri, 07 Oct 2016 05:22:11 -0500

AccNET is a Perl application that presents a new way to study the accessory genome of a given set of organisms. Using the proteomes of these organisms, AccNET create a bipartite network compatible with common network analysis platforms. AccNET collects phylogenetic and functional information in a network improving the analysis capability. Networks offer a new perspective of organism organization through elements acquired by horizontal gene transfers and not constricted by hierarchical structures.

More at https://www.youtube.com/watch?v=vdGuy1GAJrQ

Address of the bookmark: https://sourceforge.net/projects/accnet/

SATSUMA : Highly sensitive whole-genome synteny alignments.

Jit — Fri, 13 May 2016 05:25:26 -0500

Satsuma is a whole-genome synteny alignment program. It takes two genomes, computes alignments, and then keeps only the parts that are orthologous, i.e. following the conserved order and orientation of features, such as protein coding genes, non-coding genes, or neutral sequences. Satsuma does not require any pre-processing, such as repeat masking, since it will automatically detect ambiguous mappings.

Satsuma has parallelization built-in and is designed to run on multi-core architectures. The run-time for aligning two bird-size genomes (~1.2 Gb) is around two days on 24 CPUs.

You can find the manual here.
Download the latest source code from here.
Stable versions can also be downloaded from the Broad Institute's web site.

An incomplete list of questions and answers (yes, these have really been asked by our users! Please feel free to add your own by e-mailing us) is here.

If you use Satsuma in your research, please cite:
Grabherr, M. G., Russell, P., Meyer, M., Mauceli, E., Alföldi, J., Di Palma, F., & Lindblad-Toh, K. (2010). Genome-wide synteny through highly sensitive sequence alignment: Satsuma. Bioinformatics, 26(9), 1145-51.

Tutorial at http://evomics.org/learning/genomics/satsuma/

Address of the bookmark: http://satsuma.sourceforge.net/