<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/42310?offset=10 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/43801/smudgeplot-inference-of-ploidy-and-heterozygosity-structure-using-whole-genome-sequencing-data Fri, 25 Feb 2022 04:42:09 -0600 https://bioinformaticsonline.com/bookmarks/view/43801/smudgeplot-inference-of-ploidy-and-heterozygosity-structure-using-whole-genome-sequencing-data <![CDATA[Smudgeplot: Inference of ploidy and heterozygosity structure using whole genome sequencing data]]> This tool extracts heterozygous kmer pairs from kmer count databases and performs gymnastics with them. We are able to disentangle genome structure by comparing the sum of kmer pair coverages (CovA + CovB) to their relative coverage (CovB / (CovA + CovB)). Such an approach also allows us to analyze obscure genomes with duplications, various ploidy levels, etc.

Smudgeplots are computed from raw or even better from trimmed reads and show the haplotype structure using heterozygous kmer pairs. For example:

smudgeexample

Address of the bookmark: https://github.com/KamilSJaron/smudgeplot

]]> Neel https://bioinformaticsonline.com/bookmarks/view/36755/minialign-fast-and-accurate-alignment-tool-for-pacbio-and-nanopore-long-reads Thu, 24 May 2018 08:33:26 -0500 https://bioinformaticsonline.com/bookmarks/view/36755/minialign-fast-and-accurate-alignment-tool-for-pacbio-and-nanopore-long-reads <![CDATA[minialign: fast and accurate alignment tool for PacBio and Nanopore long reads]]> Address of the bookmark: https://github.com/ocxtal/minialign

]]> Jit 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/43856/puffaligner-a-fast-efficient-and-accurate-aligner-based-on-the-pufferfish-index Thu, 21 Apr 2022 05:41:39 -0500 https://bioinformaticsonline.com/bookmarks/view/43856/puffaligner-a-fast-efficient-and-accurate-aligner-based-on-the-pufferfish-index <![CDATA[PuffAligner: a fast, efficient and accurate aligner based on the Pufferfish index]]> PuffAligner, a fast, accurate and versatile aligner built on top of the Pufferfish index. PuffAligner is able to produce highly sensitive alignments, similar to those of Bowtie2, but much more quickly. While exhibiting similar speed to the ultrafast STAR aligner, PuffAligner requires considerably less memory to construct its index and align reads. PuffAligner strikes a desirable balance with respect to the time, space and accuracy tradeoffs made by different alignment tools and provides a promising foundation on which to test new alignment ideas over large collections of sequences.

Address of the bookmark: https://github.com/COMBINE-lab/pufferfish/tree/cigar-strings

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/44902/hite-a-fast-and-accurate-dynamic-boundary-adjustment-approach-for-full-length-transposable-elements-detection-and-annotation-in-genome-assemblies Sat, 20 Sep 2025 09:34:04 -0500 https://bioinformaticsonline.com/bookmarks/view/44902/hite-a-fast-and-accurate-dynamic-boundary-adjustment-approach-for-full-length-transposable-elements-detection-and-annotation-in-genome-assemblies <![CDATA[HiTE: a fast and accurate dynamic boundary adjustment approach for full-length Transposable Elements detection and annotation in Genome Assemblies]]> HiTE is a Python software that uses a dynamic boundary adjustment approach to detect and annotate full-length Transposable Elements in Genome Assemblies. In comparison to other tools, HiTE demonstrates superior performance in detecting a greater number of full-length TEs.

panHiTE

We have developed panHiTE, a comprehensive and accurate pipeline for TE detection in large-scale population genomes. It has been successfully applied to hundreds of plant population genomes, demonstrating its effectiveness and scalability.

For detailed instructions, please refer to the panHiTE tutorial.

Address of the bookmark: https://github.com/CSU-KangHu/HiTE

]]> LEGE https://bioinformaticsonline.com/bookmarks/view/42415/sneakysnake-a-fast-and-accurate-universal-genome-pre-alignment-filter-for-cpus-gpus-and-fpgas Sun, 20 Dec 2020 01:39:54 -0600 https://bioinformaticsonline.com/bookmarks/view/42415/sneakysnake-a-fast-and-accurate-universal-genome-pre-alignment-filter-for-cpus-gpus-and-fpgas <![CDATA[SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs]]> The first and the only pre-alignment filtering algorithm that works efficiently and fast on modern CPU, FPGA, and GPU architectures. SneakySnake greatly (by more than two orders of magnitude) expedites sequence alignment calculation for both short (Illumina) and long (ONT and PacBio) reads. Described by Alser et al. (preliminary version at https://arxiv.org/abs/1910.09020).

Address of the bookmark: https://github.com/CMU-SAFARI/SneakySnake

]]> Neel https://bioinformaticsonline.com/bookmarks/view/33461/graphmap-a-highly-sensitive-and-accurate-mapper-for-long-error-prone-reads Wed, 07 Jun 2017 04:18:16 -0500 https://bioinformaticsonline.com/bookmarks/view/33461/graphmap-a-highly-sensitive-and-accurate-mapper-for-long-error-prone-reads <![CDATA[GraphMap - A highly sensitive and accurate mapper for long, error-prone reads]]> GraphMap - A highly sensitive and accurate mapper for long, error-prone reads http://www.nature.com/ncomms/2016/160415/ncomms11307/full/ncomms11307.html

Features

    Mapping position agnostic to alignment parameters.
    Consistently very high sensitivity and precision across different error profiles, rates and sequencing technologies even with default parameters.
    Circular genome handling to resolve coverage drops near ends of the genome.
    E-value.
    Meaningful mapping quality.
    Various alignment strategies (semiglobal bit-vector and Gotoh, anchored).
    Overlapping of reads for de novo assembly.
    Transcriptome mapping through internal construction of a transcriptome from a given genomic reference and a GTF file.
    ...and much more.

GraphMap is also used as an overlapper in a new de novo genome assembly project called Ra (https://github.com/mariokostelac/ra-integrate).
Ra attempts to create de novo assemblies from raw nanopore and PacBio reads without requiring error correction, for which a highly sensitive overlapper is required.

Currently, development of a new spliced-alignment mode for mapping RNA-seq reads is under way.
Description of the current effort as well as how to reach the experimental implementation can be found here: doc/rnaseq.md.

Address of the bookmark: https://github.com/isovic/graphmap

]]> Jit https://bioinformaticsonline.com/bookmarks/view/34543/acana-an-accurate-and-consistent-alignment-tool-for-dna-sequences Wed, 06 Dec 2017 09:45:29 -0600 https://bioinformaticsonline.com/bookmarks/view/34543/acana-an-accurate-and-consistent-alignment-tool-for-dna-sequences <![CDATA[ACANA: An accurate and consistent alignment tool for DNA sequences]]> ACANA is an accurate and consistent alignment tool for DNA sequences. ACANA is specifically designed for aligning sequences that share only some moderately conserved regions and/or have a high frequency of long insertions or deletions. It attempts to combine the best of local and global alignments algorithms in searching for evolutionarily related regions of sequences in order to achieve the best alignment. ACANA is also robust to the small changes of alignment parameters, particularly the gap extension score. As an accurate alignment tool, ACANA is particularly useful in comparative sequence analysis for identifying conserved functional regulatory elements.

Address of the bookmark: https://www.niehs.nih.gov/research/resources/software/biostatistics/acana/index.cfm

]]> Jit https://bioinformaticsonline.com/bookmarks/view/40889/rcorrector-efficient-and-accurate-error-correction-for-illumina-rna-seq-reads Tue, 04 Feb 2020 23:23:16 -0600 https://bioinformaticsonline.com/bookmarks/view/40889/rcorrector-efficient-and-accurate-error-correction-for-illumina-rna-seq-reads <![CDATA[Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads]]> 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/

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