BOL: Related items

Download assemblies from NCBI

Bulbul — Mon, 15 May 2017 06:02:32 -0500

A new “Download assemblies” button is now available in the Assembly database. This makes it easy to download data for multiple genomes without having to write scripts.

For example, you can run a search in Assembly and use check boxes (see left side of screenshot below) to refine the set of genome assemblies of interest. Then, just open the “Download assemblies” menu, choose the source database (GenBank or RefSeq), choose the file type, and start the download. An archive file will be saved to your computer that can be expanded into a folder containing your selected genome data files.

More at https://ncbiinsights.ncbi.nlm.nih.gov/2017/05/08/genome-data-download-made-easy/

Rectangle Graph for Repeat Resolution in Genome Assembly

Rahul Nayak — Thu, 28 Dec 2017 09:43:03 -0600

Ultimate tool for resolving repeats in genome assemblies.

Though the specific implementation of the idea of the rectangle graph approach is already included into the current SPAdes distribution, we're also releasing the Rectangle Graph Module (RGM) as the separate code which can be run independently of SPAdes. Although RGM differs from the current implementation of the rectangle graph approach in SPAdes, in the future we plan to integrate RGM in SPAdes. RGM can be run with other genome assemblers if they use the graph format as SPAdes files.

For more details see: Nikolay Vyahhi, Son K. Pham, Pavel Pevzner. From de Bruijn Graphs to Rectangle Graphs for Genome Assembly, Lecture Notes in Bioinformatics 7534 (2012), pp. 249-261.

Address of the bookmark: http://bioinf.spbau.ru/en/rectangles

The MARVEL assembler

Jit — Fri, 04 May 2018 19:18:41 -0500

MARVEL consists of a set of tools that facilitate the overlapping, patching, correction and assembly of noisy (not so noisy ones as well) long reads.

The assembly process can be summarized as follows:

overlap
patch reads
overlap (again)
scrubbing
assembly graph construction and touring
optional read correction
fasta file creation

Address of the bookmark: https://github.com/schloi/MARVEL

BAUM – Improving Genome Assembly by Adaptive Unique Mapping and Local Overlap-Layout-Consensus

Jit — Wed, 24 Oct 2018 23:35:09 -0500

BAUM, breaks the whole genome into regions by adaptive unique mapping; then the local OLC is used to assemble each region in parallel. BAUM can: (1) perform reference-assisted assembly based on the genome of a close species; (2) or improve the results of existing assemblies that are obtained based on short or long sequencing reads.

Address of the bookmark: http://www.zhanyuwang.xin/wordpress/index.php/2017/07/21/baum-improving-genome-assembly-by-adaptive-unique-mapping-and-local-overlap-layout-consensus/

Genobuntu: Package for Next Generation Sequencing and Genome Assembly

BioStar — Mon, 18 May 2020 16:47:56 -0500

Genobuntu is a software package containing more than 70 software and packages oriented towards NGS. In its current version, Genobuntu supports pre assembly tools, genome assemblers as well as post assembly tools.

Commonly used biological software and example script files for different assembly pipelines have also been provided, where the example script files can be updated to suit one’s experimental needs. Genobuntu attempts to reduce the amount of time and energy needed to build software workstations and it can also act as a good teaching source for a class room setting.

Therefore, Genobuntu offers a well-tailored environment for both novices and experts working in the field of genome assembly.

Features

Velvet
MiB
SSAKE
EULER
VCAKE
ABySS
ALLPATHS
Celera
SHARCGS
Allpaths
IDBA
TAIPAN
Edena
SOAPdenovo
Maq
IDBA-UD
No. of Reads present in the Ref. Seq.
ART NGS Reads Simulator
HiTEC, FASTQC
Minimum Description Length
SOAPaligner
Sequencing Read Archive Toolkit

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

QuasiModo - Quasispecies Metric Determination on Omics

Neel — Sat, 26 Jun 2021 15:22:56 -0500

This repository contains the scripts and pipeline that reproduces the results of the HCMV benchmarking study. In this study we evaluated genome assemblers and variant callers on 10 in vitro generated, mixed strain HCMV sequence samples, each consisting of two lab strains in different abundance ratios. This tool can also be used to evaluate assemblies and variant calling results on other similar datasets.

https://academic.oup.com/bib/article/22/3/bbaa123/5868070

Address of the bookmark: https://github.com/hzi-bifo/Quasimodo

ALE: Assembly Likelihood Estimator

BioStar — Wed, 08 Mar 2023 01:39:33 -0600

Just import the assembly, bam and ALE scores. You can convert the .ale file to a set of .wig files with ale2wiggle.py and IGV can read those directly. Depending on your genome size you may want to convert the .wig files to the BigWig format.

Address of the bookmark: https://github.com/sc932/ALE

Converting FASTQ to FASTA

Neel — Fri, 12 Jan 2018 03:49:09 -0600

There are several ways you can convert fastq to fasta sequences. Some methods are listed below.

Using SED

sed can be used to selectively print the desired lines from a file, so if you print the first and 2rd line of every 4 lines, you get the sequence header and sequence needed for fasta format.

sed -n '1~4s/^@/>/p;2~4p' INFILE.fastq > OUTFILE.fasta

Using PASTE

You can linerize every 4 lines in a tabular format and print first and second field using paste

cat INFILE.fastq | paste - - - - |cut -f 1, 2| sed 's/@/>/'g | tr -s "/t" "/n" > OUTFILE.fasta

EMBOSS:seqret

Standard script that can be used for many purposes. One such use is fastq-fasta conversion

seqret -sequence reads.fastq -outseq reads.fasta

awk can be used for conversion as follows:

Using AWK

cat infile.fq | awk '{if(NR%4==1) {printf(">%s\n",substr($0,2));} else if(NR%4==2) print;}' > file.fa

FASTX-toolkit

fastq_to_fasta is available in the FASTX-toolkit that scales really well with the huge datasets

fastq_to_fasta -h
usage: fastq_to_fasta [-h] [-r] [-n] [-v] [-z] [-i INFILE] [-o OUTFILE]
# Remember to use -Q33 for illumina reads!
version 0.0.6
       [-h]         = This helpful help screen.
       [-r]         = Rename sequence identifiers to numbers.
       [-n]         = keep sequences with unknown (N) nucleotides.
                   Default is to discard such sequences.
       [-v]         = Verbose - report number of sequences.
                   If [-o] is specified,  report will be printed to STDOUT.
                   If [-o] is not specified (and output goes to STDOUT),
                   report will be printed to STDERR.
       [-z]         = Compress output with GZIP.
       [-i INFILE]  = FASTA/Q input file. default is STDIN.
       [-o OUTFILE] = FASTA output file. default is STDOUT.

Bioawk

Another option to convert fastq to fasta format using bioawk

bioawk -c fastx '{print ">"$name"\n"$seq}' input.fastq > output.fasta

Seqtk

From the same developer, there is another option using a tool called seqtk

seqtk seq -a input.fastq > output.fasta

Note that you can use either compressed or uncompressed files for this tool

UniqueKmer: Generate unique KMERs for every contig in a FASTA file

Abhi — Fri, 17 Dec 2021 00:08:15 -0600

Generate unique k-mers for every contig in a FASTA file.

Unique k-mer is consisted of k-mer keys (i.e. ATCGATCCTTAAGG) that are only presented in one contig, but not presented in any other contigs (for both forward and reverse strands).

This tool accepts the input of a FASTA file consisting of many contigs, and extract unique k-mers for each contig.

The output unique k-mer file and Genome file can be used for fastv: https://github.com/OpenGene/fastv, which is an ultra-fast tool to identify and visualize microbial sequences from sequencing data.

https://github.com/OpenGene/UniqueKMER

Address of the bookmark: https://github.com/OpenGene/UniqueKMER

AirLift, a methodology and tool for comprehensively moving mappings and annotations from one genome to another similar genome

Jit — Mon, 23 Dec 2019 10:20:13 -0600

We propose AirLift, a methodology and tool for comprehensively moving mappings and annotations from one genome to another similar genome while maintaining the accuracy of a full mapper.

Address of the bookmark: https://github.com/CMU-SAFARI/AirLift