BOL: Related items

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

Centurion

Jit — Fri, 12 Feb 2016 04:45:41 -0600

Although centromeres are essential for life and are the subject of extensive research, centromere locations in yeast genomes are difficult to infer, and in most species they are still unknown. Recently, the chromatin conformation assay Hi-C has been re-purposed for diverse applications, including de novo genome assembly, deconvolution of metagenomic samples, and inference of centromere locations. We describe a method, Centurion, that jointly infers the locations of all centromeres in a single yeast genome by exploiting the centromeres’ tendency to cluster in 3D space. We first demonstrate the accuracy of Centurion in identifying known centromere locations from high coverage Hi-C data of budding yeast and a human malaria parasite. We then use two metagenomic samples with relatively low coverage Hi-C data to infer centromere locations for each chromosome in 14 different yeast species. For yeasts with large centromeres (e.g., S. pombe) Centurion predicts the exact centromere locations. For seven yeasts with point centromeres, Centurion predicts most of the centromeres at an average of 5~kb distance from their known locations. Finally, we predict centromere coordinates for six yeast species that currently lack centromere annotations. These results suggest that Centurion can be used for centromere identification for a large number of yeast species, even with a limited amount of Hi-C sequencing.

Paper:http://www.ncbi.nlm.nih.gov/pubmed/25940625

More at http://cbio.ensmp.fr/centurion/

Address of the bookmark: http://cbio.ensmp.fr/centurion/

Radka Reifová Lab

Mon, 15 Feb 2016 06:00:48 -0600

We are generally interested in the mechanisms of species origin from a molecular and ecological perspective. Particularly, we are interested in the role of sex chromosomes in speciation. Most of our research is done on birds and mammals. Currently, we focus our research on two hybridizing song birds, the Common nightingale (Luscinia megarhynchos) and the Thrush Nightingale (L. luscinia). Combining population genomic and ecological approaches we try to elucidate the genetic architecture of reproductive isolation and understand the role of interspecific competition and song convergence in the evolution of reproductive isolation between the species.

More at http://web.natur.cuni.cz/~radkas/index.php?page=research

Genome Browser : GBrowse

Jit — Fri, 19 Feb 2016 09:22:43 -0600

Generic Genome Browser Version 2: A Tutorial for Administrators

This is an extensive tutorial to take you through the main features and gotchas of configuring GBrowse as a server. This tutorial assumes that you have successfully set up Perl, GD, BioPerl and the other GBrowse dependencies. If you haven't, please see the GBrowse HOWTO During most of the tutorial, we will be using the "in-memory" GBrowse database (no relational database required!) Later we will show how to set up a genome size database using the berkeleydb and MySQL adaptors.

More at http://elp.ucdavis.edu/tutorial/tutorial.html

Address of the bookmark: http://elp.ucdavis.edu/tutorial/tutorial.html

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/

scikit-learn

Jitendra Prajapati — Mon, 29 Feb 2016 17:39:24 -0600

Machine Learning in Python

Simple and efficient tools for data mining and data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable - BSD license

More at http://scikit-learn.org/stable/index.html

Address of the bookmark: http://scikit-learn.org/stable/auto_examples/index.html

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

PhyloGrapher - Graph Visualization Tool

Jitendra Prajapati — Wed, 06 Apr 2016 19:06:48 -0500

PhyloGrapher is a program designed to visualize and study evolutionary relationships within families of homologous genes or proteins (elements).PhyloGrapher is a drawing tool that generates custom graphs for a given set of elements. In general, it is possible to use PhyloGrapher to visualize any type of relations between elements.

More at http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html

Address of the bookmark: http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html

DISCOVAR

Abhimanyu Singh — Mon, 18 Apr 2016 11:59:16 -0500

DISCOVAR is a new variant caller and DISCOVAR de novo a new genome assembler, both designed for state-of-the-art data. Their inputs are chosen to optimize quality while keeping costs low. Currently it takes as input Illumina reads of length 250 or longer — produced on MiSeq or HiSeq 2500 — and from a single PCR-free library. These data enable a level of completeness and continuity that was not previously possible.

DISCOVAR can call variants on a region by region basis, potentially tiling an entire large genome. DISCOVAR variant calling is under active development and transitioning to VCF.

DISCOVAR de novo can generate de novo assemblies for both large and small genomes. It currently does not call variants.

More at https://www.broadinstitute.org/software/discovar/blog/?page_id=14

Address of the bookmark: https://www.broadinstitute.org/software/discovar/blog/

HOMER: Software for motif discovery and next-gen sequencing analysis

Neel — Tue, 26 Apr 2016 03:48:23 -0500

This tutorial covers topics independently of HOMER, and represents knowledge which is important to know before diving head first into more advanced analysis tools such as HOMER.

Setting up your computing environment
Retrieving and storing sequencing files (your own data or from public sources)
Checking sequence quality, trimming, general sequence manipulation
Mapping reads to a reference genome
Manipulating SAM/BAM alignment files
Visualizing data in a genome browser

RNA-Seq

Microarray

Basic analysis of Affymetrix Gene Expression Arrays using R/Bioconductor

General Tips for Data Analysis

Excel workarounds, adding gene annotation, X-Y plots tips, etc.

Address of the bookmark: http://homer.salk.edu/homer/basicTutorial/