BOL: Related items

HiTE: a fast and accurate dynamic boundary adjustment approach for full-length Transposable Elements detection and annotation in Genome Assemblies

LEGE — Sat, 20 Sep 2025 09:34:04 -0500

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

A Subway Map for the Genome

LEGE — Fri, 04 Sep 2026 04:14:14 -0500

Maya was looking at a genome graph on her computer. There were nodes, branches, and paths going in every direction.

“It’s like a subway map,” she said, “but for DNA.”

That idea is exactly what Sequence Tube Map brings to life.

Instead of showing a genome as one long, confusing sequence, it displays different genomic paths like routes on a metro map. Where sequences are shared, the paths travel together. Where genetic variation occurs, they branch into different routes and may join again later.

This makes complex genome graphs much easier to explore.

Maya could now see how different haplotypes moved through the same genomic region and where they took different paths. She could even view sequencing reads mapped onto the graph, helping her understand what was happening in regions with genetic variation.

What once looked like a tangled network now looked like a journey.

Sequence Tube Map turns the complexity of graph genomes into a visual story—making it easier for scientists to see how DNA can take different routes.

Read more at https://github.com/vgteam/sequencetubemap

and

https://academic.oup.com/bioinformatics/article/35/24/5318/5542397

Demo at https://vgteam.github.io/sequenceTubeMap/

The Genes That Travel Together: A Genomic Detective Story

BioStar — Fri, 11 Sep 2026 13:00:36 -0500

Imagine looking through thousands of microbial genomes and discovering two genes that repeatedly appear together. The obvious conclusion is that they must somehow be connected—that perhaps they work together, participate in the same pathway, or depend on each other. But evolution has a way of leaving misleading clues. What if these two genes are found together simply because they were inherited from the same ancient ancestor? In that case, their apparent association may have little to do with their biological function; it may simply be a reflection of shared evolutionary history. This is the intriguing problem addressed by CORGIAS (https://github.com/ynishimuraLv/corgias), a computational framework developed to distinguish genuine gene associations from correlations created by common ancestry.

Instead of asking only whether two genes occur together across genomes, CORGIAS asks a deeper question: did these genes actually evolve together, repeatedly gaining or losing their presence in concert, or are they merely travelling together because of their inherited history? The framework introduces two complementary approaches, Ancestral State Adjustment (ASA) and the Simultaneous EVolution test (SEV), which incorporate evolutionary information into the search for gene associations. This distinction becomes increasingly important as genome sequencing continues to uncover enormous numbers of microbial genomes, many from organisms that have never been cultured and whose genes remain functionally mysterious. In this growing genomic landscape, simply finding a gene is no longer enough—we need clues about what that gene might be doing. CORGIAS approaches this problem almost like a genomic detective: rather than treating every association as evidence, it reconstructs the evolutionary story behind the association and asks whether the evidence survives that history. By doing so, it offers a way to separate coincidence from biological connection and potentially uncover functional relationships hidden within the vast microbial genomic landscape. The broader message is beautifully simple: genes do not evolve in isolation, and understanding what they do may require us to look beyond where they are today and reconstruct how they arrived there. Sometimes, the most important clue in a genome is not the gene itself, but the evolutionary journey it has taken.

More at https://academic.oup.com/nargab/article/7/4/lqaf182/8377910?

LSC :a long read error correction tool

Jit — Thu, 02 Aug 2018 07:39:46 -0500

Getting Started

These simple steps will help you integrate LSC into your transcriptomics analysis pipeline.

Read the LSC_requirements for running LSC.
Download and set-up the LSC package.
Follow the tutorial to see how LSC works on some example data.
Read the manual if anything is unclear.
You're ready, Happy LSCing!

Latest publication

Kin Fai Au, Jason Underwood, Lawrence Lee and Wing Hung Wong
Improving PacBio Long Read Accuracy by Short Read Alignment [Manuscript]
PLoS ONE 2012. 7(10): e46679. doi:10.1371/journal.pone.0046679

Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/LSC/

COSINE: non-seeding method for mapping long noisy sequences

Jit — Fri, 26 Oct 2018 00:41:59 -0500

Third generation sequencing (TGS) are highly promising technologies but the long and noisy reads from TGS are difficult to align using existing algorithms. Here, we present COSINE, a conceptually new method designed specifically for aligning long reads contaminated by a high level of errors.

Address of the bookmark: https://github.com/SUwonglab/COSINE

LncPipe:A Nextflow-based pipeline for comprehensive analyses of long non-coding RNAs from RNA-seq datasets

LEGE — Fri, 17 Sep 2021 01:57:02 -0500

The pipeline was developed based on a popular workflow framework Nextflow, composed of four core procedures including reads alignment, assembly, identification and quantification. It contains various unique features such as well-designed lncRNAs annotation strategy, optimized calculating efficiency, diversified classification and interactive analysis report. LncPipe allows users additional control in interuppting the pipeline, resetting parameters from command line, modifying main script directly and resume analysis from previous checkpoint.

Ref https://www.lncrnablog.com/lncpipe-a-nextflow-based-pipeline-for-identification-and-analysis-of-long-non-coding-rnas-from-rna-seq-data/

Address of the bookmark: https://github.com/likelet/LncPipe

lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data

BioJoker — Tue, 27 Nov 2018 04:43:57 -0600

lordFAST is a sensitive tool for mapping long reads with high error rates. lordFAST is specially designed for aligning reads from PacBio sequencing technology but provides the user the ability to change alignment parameters depending on the reads and application.

lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint.

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

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/

BlasR Mapping single molecule sequencing reads using Basic Local Alignment with Successive Refinement (BLASR): Theory and Application,

Jit — Wed, 23 May 2018 06:54:32 -0500

BLASR (Basic Local Alignment with Successive Refinement) for mapping Single Molecule Sequencing (SMS) reads that are thousands to tens of thousands of bases long with divergence between the read and genome dominated by insertion and deletion error.

Here is how I use the blasr to align PacBio reads to the contigs (target.fasta). The “target.fasta.sa” is the suffix array from “target.fasta” generated by sawriter.

blasr query.fa ./target.fasta -sa ./target.fasta.sa -bestn 40 -maxScore -500 -m 4 -nproc 24 -out target.m4 -maxLCPLength 15

the output format option “-m 4″ generate the alignment coordinate. Not fully documented, but I can explain that to you.

I use a 24 cores / 48G ram server for the alignment. It took about 2 to 3 hours aligning 3G PacBio Reads to 10^6 sequences of short read contigs with a mean 3.5kbp length.

Address of the bookmark: http://bix.ucsd.edu/projects/blasr/

Breakpointer: using local mapping artifacts to support sequence breakpoint discovery from single-end reads

Jit — Tue, 12 Jun 2018 12:41:10 -0500

Breakpointer is a fast tool for locating sequence breakpoints from the alignment of single end reads (SE) produced by next generation sequencing (NGS). It adopts a heuristic method in searching for local mapping signatures created by insertion/deletions (indels) or more complex structural variants(SVs).

Address of the bookmark: https://github.com/ruping/Breakpointer