<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/41459?offset=30 https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results Sun, 12 Apr 2020 10:02:22 -0500 https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results <![CDATA[WEGO : simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results]]> WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. As the GO vocabulary became more and more popular, WEGO was widely adopted and used in many researches. Therefore we have updated WEGO 2.0 in 2018. Here are some changes we’ve made:
1. The limit of input file numbers was cancelled. Now the users could upload as many files as they want with one operation.
2. We have added the reference data of 9 species for users selection.
3. Besides the traditional WEGO histogram, WEGO 2.0 outputs an additional type of bar graph showing GO terms with significant gene number differences.

Address of the bookmark: http://wego.genomics.org.cn/

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/38579/genomeview-genome-browser-and-annotation-editor Wed, 02 Jan 2019 04:09:06 -0600 https://bioinformaticsonline.com/bookmarks/view/38579/genomeview-genome-browser-and-annotation-editor <![CDATA[GenomeView: genome browser and annotation editor]]> GenomeView is a genome browser and annotation editor that displays reference sequence, annotation, multiple alignments, short read alignments and graphs. Most major data formats are supported. Local and internet files can be loaded.
This project has moved to GitHub: https://github.com/GenomeView/genomeview

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

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/34528/cope-an-accurate-k-mer-based-pair-end-reads-connection-tool-to-facilitate-genome-assembly Wed, 06 Dec 2017 02:08:14 -0600 https://bioinformaticsonline.com/bookmarks/view/34528/cope-an-accurate-k-mer-based-pair-end-reads-connection-tool-to-facilitate-genome-assembly <![CDATA[COPE: an accurate k-mer-based pair-end reads connection tool to facilitate genome assembly]]> An efficient tool called Connecting Overlapped Pair-End (COPE) reads, to connect overlapping pair-end reads using k-mer frequencies. We evaluated our tool on 30× simulated pair-end reads from Arabidopsis thaliana with 1% base error. COPE connected over 99% of reads with 98.8% accuracy, which is, respectively, 10 and 2% higher than the recently published tool FLASH. When COPE is applied to real reads for genome assembly, the resulting contigs are found to have fewer errors and give a 14-fold improvement in the N50 measurement when compared with the contigs produced using unconnected reads.

Address of the bookmark: ftp://ftp.genomics.org.cn/pub/cope

]]> Jit https://bioinformaticsonline.com/bookmarks/view/41673/lr-gapcloser-a-tiling-path-based-gap-closer-that-uses-long-reads-to-complete-genome-assembly Thu, 14 May 2020 15:09:52 -0500 https://bioinformaticsonline.com/bookmarks/view/41673/lr-gapcloser-a-tiling-path-based-gap-closer-that-uses-long-reads-to-complete-genome-assembly <![CDATA[LR_Gapcloser: a tiling path-based gap closer that uses long reads to complete genome assembly]]> LR_Gapcloser is a gap closing tool using long reads from studied species. The long reads could be downloaed from public read archive database (for instance, NCBI SRA database ) or be your own data. Then they are fragmented and aligned to scaffolds using BWA mem algorithm in BWA package. In the package, we provided a compiled bwa, so the user needn't to install bwa. LR_Gapcloser uses the alignments to find the bridging that cross the gap, and then fills the long read original sequence into the genomic gaps.

Address of the bookmark: https://github.com/CAFS-bioinformatics/LR_Gapcloser

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/38413/genobuntu-a-software-package-containing-more-than-70-software-and-packages-oriented-towards-ngs-and-genome-assembly Tue, 11 Dec 2018 05:15:57 -0600 https://bioinformaticsonline.com/bookmarks/view/38413/genobuntu-a-software-package-containing-more-than-70-software-and-packages-oriented-towards-ngs-and-genome-assembly <![CDATA[Genobuntu: A software package containing more than 70 software and packages oriented towards NGS and genome assembly]]> 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. 

https://sourceforge.net/projects/genobuntu/

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

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/38735/genome-assembly-tutorial-genome-assembly-for-short-and-long-reads Sat, 19 Jan 2019 17:29:53 -0600 https://bioinformaticsonline.com/bookmarks/view/38735/genome-assembly-tutorial-genome-assembly-for-short-and-long-reads <![CDATA[Genome assembly tutorial "Genome Assembly for short and long reads"]]> In this lab we will perform de novo genome assembly of a bacterial genome. You will be guided through the genome assembly starting with data quality control, through to building contigs and analysis of the results. At the end of the lab you will know:

  1. How to perform basic quality checks on the input data
  2. How to run a short read assembler on Illumina data
  3. How to run a long read assembler on Pacific Biosciences or Oxford Nanopore data
  4. How to improve the accuracy of a long read assembly using short reads
  5. How to assess the quality of an assembly

https://bioinformaticsdotca.github.io/high-throughput_biology_2017

Address of the bookmark: https://bioinformaticsdotca.github.io/high-throughput_biology_2017_module6_lab

]]> Jit https://bioinformaticsonline.com/bookmarks/view/38801/genome-assembly-forensics-finding-the-elusive-mis-assembly Sat, 26 Jan 2019 18:02:01 -0600 https://bioinformaticsonline.com/bookmarks/view/38801/genome-assembly-forensics-finding-the-elusive-mis-assembly <![CDATA[Genome assembly forensics: finding the elusive mis-assembly]]> We present the first collection of tools aimed at automated genome assembly validation. This work formalizes several mechanisms for detecting mis-assemblies, and describes their implementation in our automated validation pipeline, called amosvalidate. We demonstrate the application of our pipeline in both bacterial and eukaryotic genome assemblies, and highlight several assembly errors in both draft and finished genomes. The software described is compatible with common assembly formats and is released, open-source, at http://amos.sourceforge.net.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2397507/ 

http://amos.sourceforge.net/wiki/index.php/AMOS

Address of the bookmark: http://amos.sourceforge.net/wiki/index.php/AMOS

]]> Jit https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads Mon, 04 May 2020 02:04:03 -0500 https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads <![CDATA[HASLR: a hybrid assembler which uses both second and third generation sequencing reads]]> HASLR, a hybrid assembler which uses both second and third generation sequencing reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples. Availability. HASLR is an open source tool available at https://github.com/vpc-ccg/haslr.

Address of the bookmark: https://github.com/vpc-ccg/haslr

]]> BioStar https://bioinformaticsonline.com/blog/view/42166/software-for-genome-assembly Sun, 30 Aug 2020 09:51:38 -0500 https://bioinformaticsonline.com/blog/view/42166/software-for-genome-assembly <![CDATA[Software for genome assembly !]]> List of bioinformatics tools/Software Website References for genome assembly:

1 Falcon https://github.com/PacificBiosciences/pb-assembly

2 Canu assembler http://canu.readthedocs.io/en/latest/index.html

3 Miniasm assembler https://github.com/lh3/miniasm

4 PBJelly scaffolding tool https://sourceforge.net/projects/pb-jelly/

5 ARCS scaffolding tool https://github.com/bcgsc/arcs

6 Redundans reduction and scaffolding tool https://github.com/Gabaldonlab/redundans

7 Arrow error correction https://github.com/PacificBiosciences/ GenomicConsensus

8 PILON error correction https://github.com/broadinstitute/pilon/wiki

9 BUSCO single copy gene markers http://busco.ezlab.org/

10 Bandage graph assembly viewer https://rrwick.github.io/Bandage/

11 Gepard dotter http://cube.univie.ac.at/gepard

12 MUMmer aligner and plotter http://mummer.sourceforge.net/

]]> LEGE https://bioinformaticsonline.com/blog/view/43634/illumina-based-assembly-pipeline-steps Fri, 10 Dec 2021 06:22:54 -0600 https://bioinformaticsonline.com/blog/view/43634/illumina-based-assembly-pipeline-steps <![CDATA[Illumina based assembly pipeline steps !]]> Illumina
  1. Merge re-sequenced FastQ files (cat)
  2. Read QC (FastQC)
  3. Adapter trimming (fastp)
  4. Removal of host reads (Kraken 2; optional)
  5. Variant calling
    1. Read alignment (Bowtie 2)
    2. Sort and index alignments (SAMtools)
    3. Primer sequence removal (iVar; amplicon data only)
    4. Duplicate read marking (picard; optional)
    5. Alignment-level QC (picard, SAMtools)
    6. Genome-wide and amplicon coverage QC plots (mosdepth)
    7. Choice of multiple variant calling and consensus sequence generation routes (iVar variants and consensus; default for amplicon data || BCFTools, BEDTools; default for metagenomics data)
      • Variant annotation (SnpEff, SnpSift)
      • Consensus assessment report (QUAST)
      • Lineage analysis (Pangolin)
      • Clade assignment, mutation calling and sequence quality checks (Nextclade)
      • Individual variant screenshots with annotation tracks (ASCIIGenome)
    8. Intersect variants across callers (BCFTools)
  6. De novo assembly
    1. Primer trimming (Cutadapt; amplicon data only)
    2. Choice of multiple assembly tools (SPAdes || Unicycler || minia)
  7. Present QC and visualisation for raw read, alignment, assembly and variant calling results (MultiQC)
]]> Surabhi Chaudhary