BOL: Related items

Reference-free prediction of rearrangement breakpoint reads

Neel — Thu, 08 Mar 2018 05:05:25 -0600

lideSort-BPR ( b reak p oint r eads) is based on a fast algorithm for all-against-all comparisons of short reads and theoretical analyses of the number of neighboring reads. When applied to a dataset with a sequencing depth of 100×, it finds ∼88% of the breakpoints correctly with no false-positive reads. Moreover, evaluation on a real prostate cancer dataset shows that the proposed method predicts more fusion transcripts correctly than previous approaches, and yet produces fewer false-positive reads. To our knowledge, this is the first method to detect breakpoint reads without using a reference genome.

https://github.com/ewijaya/slidesort-bpr

Address of the bookmark: https://code.google.com/archive/p/slidesort-bpr/

Circlator: automated circularization of genome assemblies using long sequencing reads

Poonam Mahapatra — Tue, 15 May 2018 09:42:32 -0500

A tool to circularize genome assemblies. The algorithm and benchmarks are described in the Genome Biology manuscript. Citation: "Circlator: automated circularization of genome assemblies using long sequencing reads", Hunt et al, Genome Biology 2015 Dec 29;16(1):294. doi: 10.1186/s13059-015-0849-0. PMID: 26714481.

Address of the bookmark: http://sanger-pathogens.github.io/circlator/

GenomeMapper: Simultaneous alignment of short reads against multiple genomes

Jit — Fri, 25 May 2018 09:29:44 -0500

GenomeMapper is a short read mapping tool designed for accurate read alignments. It quickly aligns millions of reads either with ungapped or gapped alignments. It can be used to align against multiple genomes simulanteously or against a single reference. If you are unsure which one is the appropriate GenomeMapper, you might want to use the latter https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2768987/

Address of the bookmark: http://1001genomes.org/software/genomemapper.html

Hercules: a profile HMM-based hybrid error correction algorithm for long reads

Jit — Mon, 20 Aug 2018 14:14:11 -0500

Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time- efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies. Results: We designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platformtextquoterights error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads. Availability:

Hercules source code is available at https://github.com/BilkentCompGen/Hercules

Address of the bookmark: https://github.com/BilkentCompGen/Hercules

LoRMA: A tool for correcting sequencing errors in long reads

Abhimanyu Singh — Thu, 06 Sep 2018 16:21:01 -0500

An error correction method that uses long reads only. The method consists of two phases: first, we use an iterative alignment-free correction method based on de Bruijn graphs with increasing length of k-mers, and second, the corrected reads are further polished using long-distance dependencies that are found using multiple alignments. According to our experiments, the proposed method is the most accurate one relying on long reads only for read sets with high coverage. Furthermore, when the coverage of the read set is at least 75×, the throughput of the new method is at least 20% higher.

conda install -c atgc-montpellier lorma

Address of the bookmark: https://gite.lirmm.fr/lorma/lorma-releases/wikis/home

Rainbow: an integrated tool for efficient clustering and assembling RAD-seq reads

Rahul Nayak — Fri, 19 Oct 2018 08:23:42 -0500

Rainbow is developed to provide an ultra-fast and memory-efficient solution to clustering and assembling short reads produced by RAD-seq. First, Rainbow clusters reads using a spaced seed method. Then, Rainbow implements a heterozygote calling like strategy to divide potential groups into haplotypes in a top–down manner. And along a guided tree, it iteratively merges sibling leaves in a bottom–up manner if they are similar enough. Here, the similarity is defined by comparing the 2nd reads of a RAD segment. This approach tries to collapse heterozygote while discriminate repetitive sequences. At last, Rainbow uses a greedy algorithm to locally assemble merged reads into contigs. Rainbow not only outputs the optimal but also suboptimal assembly results. Based on simulation and a real guppy RAD-seq data, we show that Rainbow is more competent than the other tools in dealing with RAD-seq data

Address of the bookmark: https://sourceforge.net/projects/bio-rainbow/files/

ARCS: scaffolding genome drafts with linked reads

Jit — Mon, 17 Dec 2018 17:40:28 -0600

ARCS requires two input files:

Draft assembly fasta file
Interleaved linked reads file (Barcode sequence expected in the BX tag of the read header or in the form "@readname_barcode" ; Run Long Ranger basic on raw chromium reads to produce this interleaved file)

Address of the bookmark: https://github.com/bcgsc/ARCS/

Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads

BioStar — Tue, 04 Feb 2020 23:23:16 -0600

Rcorrector has an accuracy higher than or comparable to existing methods, including the only other method (SEECER) designed for RNA-seq reads, and is more time and memory efficient. With a 5 GB memory footprint for 100 million reads, it can be run on virtually any desktop or server. The software is available free of charge under the GNU General Public License from https://github.com/mourisl/Rcorrector/.

Usage: perl run_rcorrector.pl [OPTIONS]
OPTIONS:
	Required
	-s seq_files: comma separated files for single-end data sets
	-1 seq_files_left: comma separated files for the first mate in the paried-end data sets
	-2 seq_files_right: comma separated files for the second mate in the paired-end data sets
	-i seq_files_interleaved: comma sperated files for interleaved paired-end data sets
	Optional
	-k INT: kmer_length (<=32, default: 23)
	-od STRING: output_file_directory (default: ./)
	-t INT: number of threads to use (default: 1)
	-trim : allow trimming (default: false)
	-maxcorK INT: the maximum number of correction within k-bp window (default: 4)
	-wk FLOAT: the proportion of kmers that are used to estimate weak kmer count threshold, lower for more divergent genome (default: 0.95)
	-ek INT: expected number of kmers; does not affect the correctness of program but affects the memory usage (default: 100000000)
	-stdout: output the corrected reads to stdout (default: not used)
	-verbose: output some correction information to stdout (default: not used)
	-stage INT: start from which stage (default: 0)
		0-start from begining(storing kmers in bloom filter) ;
		1-start from count kmers showed up in bloom filter;
		2-start from dumping kmer counts into a jf_dump file;
		3-start from error correction.

Address of the bookmark: https://github.com/mourisl/Rcorrector/

KAD: Assessing genome assemblies using K-mer copies in assemblies and K-mer abundance in Illumina reads

Jit — Fri, 19 Jun 2020 07:34:12 -0500

KAD is designed for evaluating the accuracy of nucleotide base quality of genome assemblies. Briefly, abundance of k-mers are quantified for both sequencing reads and assembly sequences. Comparison of the two values results in a single value per k-mer, K-mer Abundance Difference (KAD), which indicates how well the assembly matches read data for each k-mer.

where, c is the count of a k-mer from reads, m is the mode of counts of read k-mers, and n is the copy of the k-mer in the assembly.

Address of the bookmark: https://github.com/liu3zhenlab/KAD

Hifiasm: a haplotype-resolved assembler for accurate Hifi reads

Jit — Thu, 24 Dec 2020 10:03:36 -0600

Hifiasm is a fast haplotype-resolved de novo assembler for PacBio Hifi reads. It can assemble a human genome in several hours and works with the California redwood genome, one of the most complex genomes sequenced so far. Hifiasm can produce primary/alternate assemblies of quality competitive with the best assemblers. It also introduces a new graph binning algorithm and achieves the best haplotype-resolved assembly given trio data.

Address of the bookmark: https://github.com/chhylp123/hifiasm