BOL: Related items

HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies

Jit — Tue, 15 May 2018 07:35:26 -0500

HapCUT2 is a maximum-likelihood-based tool for assembling haplotypes from DNA sequence reads, designed to "just work" with excellent speed and accuracy. We found that previously described haplotype assembly methods are specialized for specific read technologies or protocols, with slow or inaccurate performance on others. With this in mind, HapCUT2 is designed for speed and accuracy across diverse sequencing technologies, including but not limited to: NGS short reads (Illumina HiSeq) clone-based sequencing (Fosmid or BAC clones) SMRT reads (PacBio) Oxford Nanopore reads 10X Genomics Linked-Reads proximity-ligation (Hi-C) reads high-coverage sequencing (>40x coverage-per-SNP) using above technologies combinations of the above technologies (e.g. scaffold long reads with Hi-C reads) See below for specific examples of command line options and best practices for some of these technologies. NOTE: At this time HapCUT2 is for diploid organisms only. VCF input should contain diploid variants. If you use HapCUT2 in your research, please cite: Edge, P., Bafna, V. & Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. gr.213462.116 (2016). doi:10.1101/gr.213462.116

Address of the bookmark: https://github.com/vibansal/HapCUT2

Ideoplot

Shruti Paniwala — Mon, 13 Feb 2017 09:47:32 -0600

Simple ideogram plotting and annotation in R.

Basic usage:

Rscript Ideoplot.R --heatmap hm.bed --annotate annotations.bed --out ideogram.pdf
-or-
Rscript Ideoplot.R --annotate annotations.bed

Options
  --ideobed, i      A bed file of reference contig lengths/chromosome names
  --heatmap, -h     Fill chromosomes with normalized heatmap
                   (described below)
  --annotate, -a    Add character annotations.
  --out, -o         PDF output name.
  --stripes, -s     Specify a file containing the layout of the
                    annotations (description below)
  --bars, -b        Add track annotations
  --reference, -f   Either hg19, or hg38
  --topdown, r      Flag, when set, flips the orientation (P arms
                    drawn on top).

Address of the bookmark: https://github.com/mchaisso/Ideoplot

transrate: Understanding your transcriptome assembly

Neel — Fri, 13 Jul 2018 07:49:26 -0500

Transrate is software for de-novo transcriptome assembly quality analysis. It examines your assembly in detail and compares it to experimental evidence such as the sequencing reads, reporting quality scores for contigs and assemblies. This allows you to choose between assemblers and parameters, filter out the bad contigs from an assembly, and help decide when to stop trying to improve the assembly.

Address of the bookmark: http://hibberdlab.com/transrate/index.html

Laj: viewing and manipulating the output from pairwise alignment programs

Abhimanyu Singh — Wed, 01 Mar 2017 08:35:40 -0600

Laj is a tool for viewing and manipulating the output from pairwise alignment programs such as blastz. It can display interactive dotplot, pip, and text representations of the alignments, a diagram showing the locations of exons and repeats, and annotation links to other web sites containing additional information about particular regions.

The program is written in Java in order to provide a graphical user interface that is portable across a variety of computer platforms; indeed its name stands for "Local Alignments with Java". Currently it exists in two forms, a stand-alone application and a web-based applet, with slightly different capabilities.

Address of the bookmark: http://www.bx.psu.edu/~ratan/

Bioinformatics opening at ICGEB NEW DELHI

Thu, 02 Mar 2017 04:16:36 -0600

ICGEB NEW DELHI

Applications are invited for:

Junior Research Fellow, in a DBT funded project, is available in Translational Health Group, ICGEB, New Delhi

Qualifications:

Education: M.Sc. (preferably in Biotechnology, Life Sciences or Zoology, Chemistry, Bioinformatics). Candidates with hands on experience on GC-MS data acquisition and analysis will be given preference. Bioinformatics expertise required.

Fellowship: As per DBT guidelines.

Tenure: The position is purely on temporary basis with an initial tenure of six months and based on satisfactory performance may continue until the completion of the project.

Closing date for applications: 04/03/2017

Please send a "TWO PAGE" CV by email to: th.icgeb@gmail.com on or before the last date.

Research Associate, in a DBT funded project, is available in Translational Health Group, ICGEB, New Delhi

Qualifications:

Education: Ph.D. (in Biology, Biotechnology, Chemistry, Bioinformatics). Candidates with hands on experience on GC-MS data acquisition and analysis will be given preference.

Fellowship: As per DBT guidelines.

Tenure: The position is purely on temporary basis with an initial tenure of six months and based on satisfactory performance may continue until the completion of the project.

Closing date for applications: 04/03/2017

Please send a "TWO PAGE" CV by email to: th.icgeb@gmail.com on or before the last date.

ALLHiC: Phasing and scaffolding polyploid genomes based on Hi-C data

BioStar — Thu, 20 Dec 2018 12:03:32 -0600

The major problem of scaffolding polyploid genome is that Hi-C signals are frequently detected between allelic haplotypes and any existing stat of art Hi-C scaffolding program links the allelic haplotypes together. To solve the problem, we developed a new Hi-C scaffolding pipeline, called ALLHIC, specifically tailored to the polyploid genomes. ALLHIC pipeline contains a total of 5 steps: prune, partition, rescue, optimize and build.

Address of the bookmark: https://github.com/tangerzhang/ALLHiC/wiki

SNPGenie

Jit — Thu, 30 Mar 2017 17:38:02 -0500

SNPGenie is a Perl script for estimating evolutionary parameters, mainly from pooled next-generation sequencing (NGS) single-nucleotide polymorphism (SNP) variant data. SNP reports (acceptable in a variety of formats) much each correspond to a single population, with variants called relative to a single reference sequence (one sequence in one FASTA file). Just run the main script, snpgenie.pl, in a directory containing the necessary input files, and we take care of the rest! For the earlier version, see Hughes Lab Bioinformatics Resource.

Address of the bookmark: https://github.com/hugheslab/snpgenie

SvABA: Genome-wide detection of structural variants and indels by local assembly

Abhimanyu Singh — Mon, 21 Jan 2019 17:58:56 -0600

SvABA is a method for detecting structural variants in sequencing data using genome-wide local assembly. Under the hood, SvABA uses a custom implementation of SGA (String Graph Assembler) by Jared Simpson, and BWA-MEM by Heng Li. Contigs are assembled for every 25kb window (with some small overlap) for every region in the genome. The default is to use only clipped, discordant, unmapped and indel reads, although this can be customized to any set of reads at the command line using VariantBam rules. These contigs are then immediately aligned to the reference with BWA-MEM and parsed to identify variants. Sequencing reads are then realigned to the contigs with BWA-MEM, and variants are scored by their read support.

Address of the bookmark: https://github.com/walaj/svaba

ChromHMM: Chromatin state discovery and characterization

Abhimanyu Singh — Wed, 19 Apr 2017 04:06:23 -0500

ChromHMM is software for learning and characterizing chromatin states. ChromHMM can integrate multiple chromatin datasets such as ChIP-seq data of various histone modifications to discover de novo the major re-occuring combinatorial and spatial patterns of marks. ChromHMM is based on a multivariate Hidden Markov Model that explicitly models the presence or absence of each chromatin mark. The resulting model can then be used to systematically annotate a genome in one or more cell types. By automatically computing state enrichments for large-scale functional and annotation datasets ChromHMM facilitates the biological characterization of each state. ChromHMM also produces files with genome-wide maps of chromatin state annotations that can be directly visualized in a genome browser.

Address of the bookmark: http://compbio.mit.edu/ChromHMM/

Mapping NGS

Abhimanyu Singh — Tue, 02 May 2017 07:58:07 -0500

NGS data are just a bunch of sequences, you have no idea which region in the genome each sequences comes from, which gene it represents...
To know that you have to align the sequences to the reference sequence. The reference sequence is in most cases the full genome sequence but sometimes, a library of EST sequences is used.
In either way, aligning your sequence reads to the reference sequence is called mapping.

The most used mappers of DNA-seq data are BWA and Bowtie for DNA-Seq data and Tophat, STAR or HISAT for RNA-Seq data. Mappers differ in which options they can take in, how fast and how accurate they are. Bowtie is faster than BWA, but looses some sensitivity (does not map an equal amount of reads to the correct position in the genome).

Address of the bookmark: http://wiki.bits.vib.be/index.php/Mapping_of_NGS_data