BOL: Related items

AlignGraph: algorithm for secondary de novo genome assembly guided by closely related references

Manisha Mishra — Tue, 17 Apr 2018 16:21:20 -0500

AlignGraph is a software that extends and joins contigs or scaffolds by reassembling them with help provided by a reference genome of a closely related organism.

Using AlignGraph

AlignGraph --read1 reads_1.fa --read2 reads_2.fa --contig contigs.fa --genome genome.fa --distanceLow distanceLow --distanceHigh distancehigh --extendedContig extendedContigs.fa --remainingContig remainingContigs.fa [--kMer k --insertVariation insertVariation --coverage coverage --part p --fastMap --ratioCheck --iterativeMap --misassemblyRemoval --resume]

Address of the bookmark: https://github.com/baoe/AlignGraph

ASplice: a scalable and memory-efficient algorithm for de novo transcriptome assembly

Rahul Nayak — Tue, 03 Jul 2018 04:09:46 -0500

With increased availability of de novo assembly algorithms, it is feasible to study entire transcriptomes of non-model organisms. While algorithms are available that are specifically designed for performing transcriptome assembly from high-throughput sequencing data, they are very memory-intensive, limiting their applications to small data sets with few libraries. Texas A&M University researchers develop a transcriptome assembly algorithm that recovers alternatively spliced isoforms and expression levels while utilizing as many RNA-Seq libraries as possible that contain hundreds of gigabases of data. New techniques are developed so that computations can be performed on a computing cluster with moderate amount of physical memory. Availability – A software program that implements the algorithm is available at: http://faculty.cse.tamu.edu/shsze/asplice. Sze SH, Pimsler ML, Tomberlin JK, Jones CD, Tarone AM. (2017) A scalable and memory-efficient algorithm for de novo transcriptome assembly of non-model organisms. BMC Genomics 18(Suppl 4):387.

Address of the bookmark: http://faculty.cse.tamu.edu/shsze/asplice/

CANU genome assembly parameters !

Rahul Nayak — Mon, 07 Jan 2019 08:40:37 -0600

Choose the appropriate parameters to run Canu and run it. The assembly will take about an hour. You can use two cores (parameter -maxThreads=2) and you would like to disable cluster option, since we compute on a single Amazon server set off the option to compute on cluster useGrid=false. This specifications should be for your project discussed with a local computing guru. The parameters that are in square brackets [] are optional, symbol | stands for "or".

usage:   canu [-correct | -trim | -assemble | -trim-assemble] \
              [-s ] \
               -p  \
               -d  \
               genomeSize=[g|m|k] \
               -maxThreads=2 \
               useGrid=false \
              [other-options] \
               read_file.fastq.gz

A default Canu run produces usually high quality assembly, example of a command that was used for testing can be found below. However, there are still a lot of parameters that are possible to tweak. For example if we desire to assemble haplotypes separately of if we want to smash them together, we can alternate the error correction process.

canu -p test_asmbl \
     -d asm_test3 \
     genomeSize=2m \
     -maxThreads=2 useGrid=false \
     -pacbio-raw \ ~/pacbio/dna/sample_reads.fastq.gz

There is a brilliant section in documentation about parameter tweaking.

The output directory contains will contain many files. The most interesting ones are:

*.correctedReads.fasta.gz : file containing the input sequences after correction, trim and split based on consensus evidence.
*.trimmedReads.fastq : file containing the sequences after correction and final trimming
*.layout : file containing informations about read inclusion in the final assembly
*.gfa : file containing the assembly graph by Canu
*.contigs.fasta : file containing everything that could be assembled and is part of the primary assembly

The basic stats of assembly can be read from reports generated by the assembler, or calculated using standard UNIX command line tools.

More at https://canu.readthedocs.io/en/latest/faq.html

NxRepair: error correction in de novo assemblies using Nextera Mate Pair Reads

BioStar — Thu, 24 Jan 2019 10:35:12 -0600

NxRepair is a python module that automatically detects large structural errors in de novo assemblies using Nextera mate pair reads. The decector will break a contig at the site of an identified misassembly and will generate a new fasta file containing both the corrected contigs and the correct, unaffected contigs.

https://nxrepair.readthedocs.io/en/latest/tutorial.html

nxrepair aligned_matepairs.bam assemblyfasta.fasta error_locations.csv new_fasta.fasta

Address of the bookmark: https://github.com/rebeccaroisin/nxrepair

RACA: Reference-Assisted Chromosome Assembly

Priya Singh — Wed, 06 Apr 2016 09:29:50 -0500

Rreference-Assisted Chromosome Assembly (RACA), an algorithm to reliably order and orient sequence scaffolds generated by NGS and assemblers into longer chromosomal fragments using comparative genome information and paired-end reads.

http://www.ncbi.nlm.nih.gov/pubmed/23307812

http://bioen-compbio.bioen.illinois.edu/RACA/

Address of the bookmark: http://bioen-compbio.bioen.illinois.edu/RACA/

Hagfish - assess an assembly through creative use of coverage plots

Abhi — Fri, 20 May 2016 19:08:17 -0500

Hagfish is a tool that is to be used in data analysis of Next Generation Sequencing (NGS) experiments. Hagfish builds on the concept of coverage plots and aims to assist (amongst others) in quality control of de novo genome assembly or identification of structural variation in a genome re-sequencing experiment.

Hagfish requires a reference sequence and a paired end re-sequencing data set. Hagfish has more power the larger the insert size of the paired end library is.

Quick links: Installation,Operation, Read mappers, Hagfish scripts, Hagfish plots

Address of the bookmark: https://github.com/mfiers/hagfish

PBSuite: Software for Long-Read Sequencing Data from PacBio

Jit — Mon, 27 Feb 2017 09:54:47 -0600

PBJelly - the genome upgrading tool.
PBHoney - the structural variation discovery tool

Both are contained within the PBSuite code found in downloads.

----- PBJelly -----
Read The Paper
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0047768

PBJelly is a highly automated pipeline that aligns long sequencing reads (such as PacBio RS reads or long 454 reads in fasta format) to high-confidence draft assembles. PBJelly fills or reduces as many captured gaps as possible to produce upgraded draft genomes.

----- PBHoney -----
Read The Paper
http://www.biomedcentral.com/1471-2105/15/180/abstract

PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.

Address of the bookmark: https://sourceforge.net/projects/pb-jelly/

GAPPadder: A Sensitive Approach for Closing Gaps on Draft Genomes with Short Sequence Reads

Jit — Mon, 14 May 2018 05:25:48 -0500

This software is provided ``as is” without warranty of any kind. In no event shall the author be held responsible for any damage resulting from the use of this software. The program package, including source codes, executables, and this documentation, is distributed free of charge. If you use this program in a publication, please cite the following reference:
Chong Chu, Xin Li, and Yufeng Wu. "GAPPadder: A Sensitive Approach for Closing Gaps on Draft Genomes with Short Sequence Reads." bioRxiv (2017): 125534.

Address of the bookmark: https://github.com/Reedwarbler/GAPPadder

ReMILO: reference assisted misassembly detection algorithm using short and long reads.

Jit — Fri, 06 Jul 2018 04:27:49 -0500

ReMILO, a reference assisted misassembly detection algorithm that uses both short reads and PacBio SMRT long reads. ReMILO aligns the initial short reads to both the contigs and reference genome, and then constructs a novel data structure called red-black multipositional de Bruijn graph to detect misassemblies. In addition, ReMILO also aligns the contigs to long reads and find their differences from the long reads to detect more misassemblies.

Address of the bookmark: https://github.com/songc001/remilo

LRCstats: a tool for evaluating long reads correction methods

Aaryan Lokwani — Wed, 22 Aug 2018 11:05:04 -0500

LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.

Address of the bookmark: https://github.com/cchauve/lrcstats