BOL: Related items

VCF Compare !

Rahul Nayak — Wed, 19 Jan 2022 10:30:14 -0600

compare two BWA mapping methods with the online hg18-mapped data

We first operate a rapid inspection of the different BAM files using samtools flagstat. Illumina provided chr21 read mapping obtained with their GA IIx deep sequencing platform <ftp://webdata:webdata@ussd-ftp.illumina.com/Data/SequencingRuns/NA18507_GAIIx_100_chr21.bam>, aligned to the b36/hg18 reference genome)

Address of the bookmark: https://wiki.bits.vib.be/index.php/NGS_Exercise.6#compare_aln_.26_mem_results_with_vcf-compare

Genomicus: genome browser that enables users to navigate in genomes in several dimensions

Jit — Mon, 28 Feb 2022 23:27:37 -0600

Genomicus is a genome browser that enables users to navigate in genomes in several dimensions: linearly along chromosome axes, transversaly across different species, and chronologicaly along evolutionary time.

Once a query gene has been entered, it is displayed in its genomic context in parallel to the genomic context of all its orthologous and paralogous copies in all the other sequenced metazoan genomes. Moreover, Genomicus stores and displays the predicted ancestral genome structure in all the ancestral species within the phylogenetic range of interest.

All the data on extant species displayed in this browser are from Ensembl.

Summary statistics of Genomicus version 105.01: (view species tree in pdf or newick)


Number of extant species	200
Number of extant genes	4303993
Number of ancestral species	196
Number of ancestral genes	4624213
Number of ancestral synteny blocks	83342

Address of the bookmark: https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl

SSPACE

Jit — Fri, 05 May 2017 05:42:15 -0500

SSPACE standard is a stand-alone program for scaffolding pre-assembled contigs using NGS paired-read data. It is unique in offering the possibility to manually control the scaffolding process. By using the distance information of paired-end and/or matepair data, SSPACE is able to assess the order, distance and orientation of your contigs and combine them into scaffolds. Currently we offer this as a command-line tool in Perl. The input data is given by pre-assembled contig sequences (FASTA) and NGS paired-read data (Illumina/454/Solid FASTA or FASTQ). The final scaffolds are provided in FASTA format.

Address of the bookmark: https://www.baseclear.com/genomics/bioinformatics/basetools/SSPACE

ContigExtender: a new approach to improving de novo sequence assembly for viral metagenomics data

LEGE — Wed, 08 May 2024 07:32:45 -0500

ContigExtender, was developed to extend contigs, complementing de novo assembly. ContigExtender employs a novel recursive Overlap Layout Candidates (r-OLC) strategy that explores multiple extending paths to achieve longer and highly accurate contigs. ContigExtender is effective for extending contigs significantly in in silico synthesized and real metagenomics datasets.

More at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7953547/

Address of the bookmark: https://github.com/dengzac/contig-extender

MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads

Rahul Nayak — Fri, 11 May 2018 05:07:45 -0500

MECAT is an ultra-fast Mapping, Error Correction and de novo Assembly Tools for single molecula sequencing (SMRT) reads. MECAT employs novel alignment and error correction algorithms that are much more efficient than the state of art of aligners and error correction tools. MECAT can be used for effectively de novo assemblying large genomes. For example, on a 32-thread computer with 2.0 GHz CPU , MECAT takes 9.5 days to assemble a human genome based on 54x SMRT data, which is 40 times faster than the current PBcR-Mhap pipeline. MECAT performance were compared with PBcR-Mhap pipeline, FALCON and Canu(v1.3) in five real datasets. The quality of assembled contigs produced by MECAT is the same or better than that of the PBcR-Mhap pipeline and FALCON.

https://www.nature.com/articles/nmeth.4432

Address of the bookmark: https://github.com/xiaochuanle/MECAT

assemblytics: delta file to analyze alignments of an assembly to another assembly or a reference genome

Jit — Thu, 14 Jun 2018 07:31:00 -0500

Download and install MUMmer Align your assembly to a reference genome using nucmer (from MUMmer package) $ nucmer -maxmatch -l 100 -c 500 REFERENCE.fa ASSEMBLY.fa -prefix OUT Consult the MUMmer manual if you encounter problems Optional: Gzip the delta file to speed up upload (usually 2-4X faster) $ gzip OUT.delta Then use the OUT.delta.gz file for upload. Upload the .delta or delta.gz file (view example) to Assemblytics Important: Use only contigs rather than scaffolds from the assembly. This will prevent false positives when the number of Ns in the scaffolded sequence does not match perfectly to the distance in the reference. The unique sequence length required represents an anchor for determining if a sequence is unique enough to safely call variants from, which is an alternative to the mapping quality filter for read alignment. http://assemblytics.com/

Address of the bookmark: http://assemblytics.com/

Referee: Genome assembly quality scores

Jit — Sun, 04 Nov 2018 16:44:30 -0600

Modern genome sequencing technologies provide a succint measure of quality at each position in every read, however all of this information is lost in the assembly process. Referee summarizes the quality information from the reads that map to a site in an assembled genome to calculate a quality score for each position in the genome assembly.

We accomplish this by first calculating genotype likelihoods for every site. For a given site in a diploid genome, there are 10 possible genotypes (AA, AC, AG, AT, CC, CG, CT, GG, GT, TT). Referee takes as input the genotype likelihoods calculated for all 10 genotypes given the called reference base at each position.

Referee is a program to calculate a quality score for every position in a genome assembly. This allows for easy filtering of low quality sites for any downstream analysis.

https://github.com/gwct/referee

Address of the bookmark: https://gwct.github.io/referee/#

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

Evaluation of genome assembly software based on long reads

BioStar — Fri, 01 Feb 2019 11:55:54 -0600

TGS technologies have been used to produce highly accurate de novo assemblies of hundreds of microbial genomes and highly contiguous reconstructions of many dozens of plant and animal genomes, enabling new insights into evolution and sequence diversity. They have also been applied to resequencing analyses, to create detailed maps of structural variations in many species. Also, these new technologies have been used to fill in many of the gaps in the human reference genome.

In this report, we compare and evaluate several genome assembly software based on TSG technology. The experimentation has been performed on 4 reference genomes and the results evaluated with the QUAST software. The 11 software that have been evaluated are: Celera Assembler , Falcon , Miniasm, Newbler , SGA Assembler, Smartdenovo, Abruijn, Ra, DBG2OLC, Spades and Cerulean. The first 8 software use only long reads, while the 3 last software can merge long and short reads

MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization

Jit — Fri, 24 Jan 2020 04:09:15 -0600

MitoZ is a Python3-based toolkit which aims to automatically filter pair-end raw data (fastq files), assemble genome, search for mitogenome sequences from the genome assembly result, annotate mitogenome (genbank file as result), and mitogenome visualization. MitoZ is available from https://github.com/linzhi2013/MitoZ.

https://academic.oup.com/nar/article/47/11/e63/5377471

Address of the bookmark: https://github.com/linzhi2013/MitoZ