<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/44902?offset=80 https://bioinformaticsonline.com/bookmarks/view/39213/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads Tue, 02 Apr 2019 21:54:55 -0500 https://bioinformaticsonline.com/bookmarks/view/39213/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads <![CDATA[Flye: Fast and accurate de novo assembler for single molecule sequencing reads]]> Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PB / ONT reads as input and outputs polished contigs. Flye also includes a special mode for metagenome assembly.

Address of the bookmark: https://github.com/fenderglass/Flye

]]> BioJoker https://bioinformaticsonline.com/bookmarks/view/41920/liftoff-an-accurate-tool-that-maps-annotations-in-gff-or-gtf-between-assemblies Tue, 30 Jun 2020 21:40:52 -0500 https://bioinformaticsonline.com/bookmarks/view/41920/liftoff-an-accurate-tool-that-maps-annotations-in-gff-or-gtf-between-assemblies <![CDATA[Liftoff: an accurate tool that maps annotations in GFF or GTF between assemblies]]>  Liftoff, an accurate tool that maps annotations in GFF or GTF between assemblies of the same, or closely-related species. Unlike current coordinate lift-over tools which require a pre-generated “chain” file as input, Liftoff is a standalone tool that takes two genome assemblies and a reference annotation as input and outputs an annotation of the target genome. 

Address of the bookmark: https://github.com/agshumate/Liftoff

]]> Jit https://bioinformaticsonline.com/bookmarks/view/44896/jaeger-an-accurate-and-fast-deep-learning-tool-to-detect-bacteriophage-sequences Sun, 31 Aug 2025 06:30:16 -0500 https://bioinformaticsonline.com/bookmarks/view/44896/jaeger-an-accurate-and-fast-deep-learning-tool-to-detect-bacteriophage-sequences <![CDATA[Jaeger : an accurate and fast deep-learning tool to detect bacteriophage sequences]]> Jaeger is a tool that utilizes homology-free machine learning to identify phage genome sequences that are hidden within metagenomes. It is capable of detecting both phages and prophages within metagenomic assemblies.

Address of the bookmark: https://github.com/MGXlab/Jaeger

]]> LEGE https://bioinformaticsonline.com/bookmarks/view/26306/busco Sun, 07 Feb 2016 16:02:39 -0600 https://bioinformaticsonline.com/bookmarks/view/26306/busco <![CDATA[BUSCO]]> Assessing genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs

More at http://busco.ezlab.org/

Address of the bookmark: http://busco.ezlab.org/

]]> Jitendra Narayan https://bioinformaticsonline.com/bookmarks/view/28997/braker-pipeline-for-fully-automated-prediction-of-protein-coding-genes-with-genemark-eset-and-augustus-in-novel-eukaryotic-genomes Thu, 01 Sep 2016 08:02:59 -0500 https://bioinformaticsonline.com/bookmarks/view/28997/braker-pipeline-for-fully-automated-prediction-of-protein-coding-genes-with-genemark-eset-and-augustus-in-novel-eukaryotic-genomes <![CDATA[BRAKER: pipeline for fully automated prediction of protein coding genes with GeneMark-ES/ET and AUGUSTUS in novel eukaryotic genomes]]> Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction.

http://www.ncbi.nlm.nih.gov/pubmed/26559507

Address of the bookmark: http://bioinf.uni-greifswald.de/bioinf/braker/

]]> Jit https://bioinformaticsonline.com/news/view/32719/download-assemblies-from-ncbi Mon, 15 May 2017 06:02:32 -0500 https://bioinformaticsonline.com/news/view/32719/download-assemblies-from-ncbi <![CDATA[Download assemblies from NCBI]]> 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.

image

 

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

]]> Bulbul https://bioinformaticsonline.com/bookmarks/view/36597/gappadder-a-sensitive-approach-for-closing-gaps-on-draft-genomes-with-short-sequence-reads Mon, 14 May 2018 05:25:48 -0500 https://bioinformaticsonline.com/bookmarks/view/36597/gappadder-a-sensitive-approach-for-closing-gaps-on-draft-genomes-with-short-sequence-reads <![CDATA[GAPPadder: A Sensitive Approach for Closing Gaps on Draft Genomes with Short Sequence Reads]]> 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

]]> Jit https://bioinformaticsonline.com/bookmarks/view/43057/hapsolo-an-optimization-approach-for-removing-secondary-haplotigs-during-diploid-genome-assembly-and-scaffolding Sat, 08 May 2021 21:25:00 -0500 https://bioinformaticsonline.com/bookmarks/view/43057/hapsolo-an-optimization-approach-for-removing-secondary-haplotigs-during-diploid-genome-assembly-and-scaffolding <![CDATA[HapSolo: An optimization approach for removing secondary haplotigs during diploid genome assembly and scaffolding]]> HapSolo, that identifies secondary contigs and defines a primary assembly based on multiple pairwise contig alignment metrics. HapSolo evaluates candidate primary assemblies using BUSCO scores and then distinguishes among candidate assemblies using a cost function. The cost function can be defined by the user but by default considers the number of missing, duplicated and single BUSCO genes within the assembly. HapSolo performs hill climbing to minimize cost over thousands of candidate assemblies. 

Address of the bookmark: https://github.com/esolares/HapSolo

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37239/kat-a-k-mer-analysis-toolkit-to-quality-control-ngs-datasets-and-genome-assemblies Fri, 06 Jul 2018 03:36:45 -0500 https://bioinformaticsonline.com/bookmarks/view/37239/kat-a-k-mer-analysis-toolkit-to-quality-control-ngs-datasets-and-genome-assemblies <![CDATA[KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies]]> KAT is a suite of tools that analyse jellyfish hashes or sequence files (fasta or fastq) using kmer counts. The following tools are currently available in KAT:

  • hist: Create an histogram of k-mer occurrences from a sequence file. Adds metadata in output for easy plotting.
  • gcp: K-mer GC Processor. Creates a matrix of the number of K-mers found given a GC count and a K-mer count.
  • comp: K-mer comparison tool. Creates a matrix of shared K-mers between two (or three) sequence files or hashes.
  • sect: SEquence Coverage estimator Tool. Estimates the coverage of each sequence in a file using K-mers from another sequence file.
  • blob: Given, reads and an assembly, calculates both the read and assembly K-mer coverage along with GC% for each sequence in the assembly.SEquence Coverage estimator Tool.
  • filter: Filtering tools. Contains tools for filtering k-mer hashes and FastQ/A files:
    • kmer: Produces a k-mer hash containing only k-mers within specified coverage and GC tolerances.
    • seq: Filters a sequence file based on whether or not the sequences contain k-mers within a provided hash.
  • plot: Plotting tools. Contains several plotting tools to visualise K-mer and compare distributions. The following plot tools are available:
    • density: Creates a density plot from a matrix created with the "comp" tool. Typically this is used to compare two K-mer hashes produced by different NGS reads.
    • profile: Creates a K-mer coverage plot for a single sequence. Takes in fasta coverage output coverage from the "sect" tool
    • spectra-cn: Creates a stacked histogram using a matrix created with the "comp" tool. Typically this is used to compare a jellyfish hash produced from a read set to a jellyfish hash produced from an assembly. The plot shows the amount of distinct K-mers absent, as well as the copy number variation present within the assembly.
    • spectra-hist: Creates a K-mer spectra plot for a set of K-mer histograms produced either by jellyfish-histo or kat-histo.
    • spectra-mx: Creates a K-mer spectra plot for a set of K-mer histograms that are derived from selected rows or columns in a matrix produced by the "comp".

In addition, KAT contains a python script for analysing the mathematical distributions present in the K-mer spectra in order to determine how much content is present in each peak.

This README only contains some brief details of how to install and use KAT. For more extensive documentation please visit: https://kat.readthedocs.org/en/latest/

https://academic.oup.com/bioinformatics/article/33/4/574/2664339 

Address of the bookmark: https://github.com/TGAC/KAT

]]> Jit https://bioinformaticsonline.com/bookmarks/view/38041/synima-a-synteny-imaging-tool-for-annotated-genome-assemblies Tue, 30 Oct 2018 10:49:13 -0500 https://bioinformaticsonline.com/bookmarks/view/38041/synima-a-synteny-imaging-tool-for-annotated-genome-assemblies <![CDATA[Synima: a Synteny imaging tool for annotated genome assemblies]]> Synima written in Perl, which uses the graphical features of R. Synima takes orthologues computed from reciprocal best BLAST hits or OrthoMCL, and DAGchainer, and outputs an overview of genome-wide synteny in PDF. Each of these programs are included with the Synima package, and a pipeline for their use. Synima has a range of graphical parameters including size, colours, order, and labels, which are specified in a config file generated by the first run of Synima – and can be subsequently edited. Synima runs quickly on a command line to generate informative and publication quality figures. Synima is open source and freely available from https://github.com/rhysf/Synima under the MIT License.

Address of the bookmark: https://github.com/rhysf/Synima

]]> Abhimanyu Singh