BOL: Related items

Minia

Jit — Thu, 08 Dec 2016 05:07:00 -0600

Minia is a short-read assembler based on a de Bruijn graph, capable of assembling a human genome on a desktop computer in a day. The output of Minia is a set of contigs. Minia produces results of similar contiguity and accuracy to other de Bruijn assemblers (e.g. Velvet).

Download

Minia 2.0.7 Linux 64-bits binaries (Source code) (Legacy codebase)

For the impatient

A typical Minia command line looks like:

./minia -in reads.fa -kmer-size 31 -abundance-min 3 -out output_prefix

Type

./minia

for a quick explanation of the parameters.

For more information, refer to the manual.

KmerGenie can be used to determine the best k-mer size, minimum abundance of correct k-mers, and genome size estimation for your dataset.

Address of the bookmark: http://minia.genouest.org/

Velvet tutorial

Poonam Mahapatra — Fri, 09 Dec 2016 04:19:07 -0600

The objective of this activity is to help you understand how to run Velvet in general, how to accurately estimate the insert size of a paired-end library through the use of Bowtie, the primary parameters of velvet, and the process involved in producing a de novo assembly from Illumina reads.

http://evomics.org/learning/assembly-and-alignment/velvet/

Address of the bookmark: http://evomics.org/learning/assembly-and-alignment/velvet/

Cutadapt

Bulbul — Wed, 14 Dec 2016 09:59:52 -0600

Cutadapt finds and removes adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.

Cutadapt helps with these trimming tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.

Cutadapt comes with an extensive suite of automated tests and is available under the terms of the MIT license.

If you use cutadapt, please cite DOI:10.14806/ej.17.1.200 .

More at https://github.com/marcelm/cutadapt

Address of the bookmark: http://cutadapt.readthedocs.io/en/stable/guide.html

GARM:Genome Assembly, Reconciliation and Merging

Jit — Mon, 19 Dec 2016 06:03:02 -0600

The pipeline is based mainly implemented using Perl scripts and modules and third-party open source software like the AMOS (Myers et al., 2000) and MUMmer (Kurtz et al., 2004) packages. The pipeline was tested on Debian, Ubuntu, Fedora and BioLinux distributions. The method merges contigs or scaffolds from different assemblers using the same or different sequencing technologies. When scaffolds are provided, a process of finding probable compressions or extensions (CE) problems in the assemblies can be per-formed; contigs are joined back into scaffolds after gap recalculation

Address of the bookmark: http://garm-meta-assem.sourceforge.net/

pyScaf

Bulbul — Mon, 19 Dec 2016 14:20:33 -0600

pyScaf orders contigs from genome assemblies utilising several types of information:

paired-end (PE) and/or mate-pair libraries (NGS-based mode)
long reads (NGS-based mode)
synteny to the genome of some related species (reference-based mode)

Scaffolding

In reference-based mode, pyScaf uses synteny to the genome of closely related species in order to order contigs and estimate distances between adjacent contigs.

Contigs are aligned globally (end-to-end) onto reference chromosomes, ignoring:

matches not satisfying cut-offs (--identity and --overlap)
suboptimal matches (only best match of each query to reference is kept)
and removing overlapping matches on reference.

In preliminary tests, pyScaf performed superbly on simulated heterozygous genomes based on C. parapsilosis (13 Mb; CANPA) and A. thaliana (119 Mb; ARATH) chromosomes, reconstructing correctly all chromosomes always for CANPA and nearly always for ARATH (Figures in dropbox, CANPA table, ARATH table).
Runs took ~0.5 min for CANPA on 4 CPUs and ~2 min for ARATH on 16 CPUs.

Important remarks:

Reduce your assembly before (fasta2homozygous.py) as any redundancy will likely break the synteny.
pyScaf works better with contigs than scaffolds, as scaffolds are often affected by mis-assemblies (no de novo assembler / scaffolder is perfect...), which breaks synteny.
pyScaf works very well if divergence between reference genome and assembled contigs is below 20% at nucleotide level.
pyScaf deals with large rearrangements ie. deletions, insertion, inversions, translocations. Note however, this is experimental implementation!
Consider closing gaps after scaffolding.

Address of the bookmark: https://github.com/lpryszcz/pyScaf

GKNO

Jit — Tue, 17 Jan 2017 03:35:34 -0600

gkno opens the world of complex bioinformatic analysis to people of all level of computational expertise. This site contains documentation, tutorials and information on all the tools that comprise gkno.

More at http://gkno.me/

Address of the bookmark: http://gkno.me/

GenomeComp

Jit — Fri, 17 Feb 2017 08:38:32 -0600

GenomeComp is a tool for summarizing, parsing and visualizing the genome wide sequence comparison results derived from voluminous BLAST textual output, so as to locate the rearrangements, insertions or deletions of genome segments between species or strains.

It can be easily used to compare, parsing and visualize large genomic sequences, especially closely related genomes such as inter-species or inter-strains. In addition, it can also show other sequence features like repeat sequence distributions in one whole-genome DNA sequence by comparing the genome to itself.

It is a stand-alone graphical user interface (GUI) program which runs on Linux, Unix, Mac OS X (tested on version 10.2.4 only) and Microsoft Windows platforms and is written in Perl/Tk.

Address of the bookmark: http://www.mgc.ac.cn/GenomeComp/

HTSlib

Jit — Wed, 15 Mar 2017 11:38:05 -0500

Samtools is a suite of programs for interacting with high-throughput sequencing data. It consists of three separate repositories:

Samtools: Reading/writing/editing/indexing/viewing SAM/BAM/CRAM format
BCFtools: Reading/writing BCF2/VCF/gVCF files and calling/filtering/summarising SNP and short indel sequence variants
HTSlib: A C library for reading/writing high-throughput sequencing data

Samtools and BCFtools both use HTSlib internally, but these source packages contain their own copies of htslib so they can be built independently.

Address of the bookmark: http://www.htslib.org/

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

Salzberg lab

Mon, 15 May 2017 05:14:01 -0500

We are a computational biology lab that develops novel methods for analysis of DNA and RNA sequences. Our research includes software for aligning and assembling RNA-seq data, whole-genome assembly, and microbiome analysis. We work closely with biomedical scientists to apply these methods to current problems arising in a broad spectrum of biological and medical research areas. We’re also part of the Center for Computational Biology, a group of 20+ faculty members and their labs at Johns Hopkins working on computational, statistical, and mathematical methods that can turn massive genomic data sets into biologically and clinically useful information.

https://salzberg-lab.org/