<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/36478?offset=480 https://bioinformaticsonline.com/blog/view/45273/a-subway-map-for-the-genome Fri, 04 Sep 2026 04:14:14 -0500 https://bioinformaticsonline.com/blog/view/45273/a-subway-map-for-the-genome <![CDATA[A Subway Map for the Genome]]> 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/

]]> LEGE https://bioinformaticsonline.com/blog/view/45306/the-genes-that-travel-together-a-genomic-detective-story Fri, 11 Sep 2026 13:00:36 -0500 https://bioinformaticsonline.com/blog/view/45306/the-genes-that-travel-together-a-genomic-detective-story <![CDATA[The Genes That Travel Together: A Genomic Detective Story]]> 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?

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/23174/scaffolding-of-a-bacterial-genome-using-minion-nanopore-sequencing Tue, 07 Jul 2015 16:59:25 -0500 https://bioinformaticsonline.com/bookmarks/view/23174/scaffolding-of-a-bacterial-genome-using-minion-nanopore-sequencing <![CDATA[Scaffolding of a bacterial genome using MinION nanopore sequencing]]> Second generation sequencing has revolutionized genomic studies. However, most genomes contain repeated DNA elements that are longer than the read lengths achievable with typical sequencers, so the genomic order of several generated contigs cannot be easily resolved. A new generation of sequencers offering substantially longer reads is emerging, notably the Pacific Biosciences (PacBio) RS II system and the MinION system, released in early 2014 by Oxford Nanopore Technologies through an early access program.

Address of the bookmark: http://www.nature.com/srep/2015/150707/srep11996/full/srep11996.html

]]> Rahul Agarwal https://bioinformaticsonline.com/bookmarks/view/36895/npscarf-real-time-scaffolder-using-spades-contigs-and-nanopore-sequencing-reads Mon, 11 Jun 2018 05:14:57 -0500 https://bioinformaticsonline.com/bookmarks/view/36895/npscarf-real-time-scaffolder-using-spades-contigs-and-nanopore-sequencing-reads <![CDATA[npScarf: real-time scaffolder using SPAdes contigs and Nanopore sequencing reads]]> Address of the bookmark: http://japsa.readthedocs.io/en/latest/tools/jsa.np.npscarf.html

]]> Shruti Paniwala https://bioinformaticsonline.com/bookmarks/view/34704/nanosim-nanopore-sequence-read-simulator-based-on-statistical-characterization Mon, 18 Dec 2017 04:16:31 -0600 https://bioinformaticsonline.com/bookmarks/view/34704/nanosim-nanopore-sequence-read-simulator-based-on-statistical-characterization <![CDATA[NanoSim: nanopore sequence read simulator based on statistical characterization.]]> NanoSim, a fast and scalable read simulator that captures the technology-specific features of ONT data and allows for adjustments upon improvement of nanopore sequencing technology. The first step of NanoSim is read characterization, which provides a comprehensive alignment-based analysis and generates a set of read profiles serving as the input to the next step, the simulation stage. The simulation stage uses the model built in the previous step to produce in silico reads for a given reference genome. NanoSim is written in Python and R. The source files and manual are available at the Genome Sciences Centre website: http://www.bcgsc.ca/platform/bioinfo/software/nanosim

https://github.com/bcgsc/NanoSim

Address of the bookmark: http://www.bcgsc.ca/platform/bioinfo/software/nanosim

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37524/fmlrc-a-long-read-error-correction-tool-using-the-multi-string-burrows-wheeler-transform Fri, 10 Aug 2018 13:29:28 -0500 https://bioinformaticsonline.com/bookmarks/view/37524/fmlrc-a-long-read-error-correction-tool-using-the-multi-string-burrows-wheeler-transform <![CDATA[FMLRC: a long-read error correction tool using the multi-string Burrows Wheeler Transform]]> FMLRC, or FM-index Long Read Corrector, is a tool for performing hybrid correction of long read sequencing using the BWT and FM-index of short-read sequencing data. Given a BWT of the short-read sequencing data, FMLRC will build an FM-index and use that as an implicit de Bruijn graph. Each long read is then corrected independently by identifying low frequency k-mers in the long read and replacing them with the closest matching high frequency k-mers in the implicit de Bruijn graph. In contrast to other de Bruijn graph based implementations, FMLRC is not restricted to a particular k-mer size and instead uses a two pass method with both a short "k-mer" and a longer "K-mer". This allows FMLRC to correct through low complexity regions that are computational difficult for short k-mers.

Address of the bookmark: https://github.com/holtjma/fmlrc

]]> Neel https://bioinformaticsonline.com/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus Fri, 05 Jan 2018 04:12:20 -0600 https://bioinformaticsonline.com/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus <![CDATA[proovread : large-scale high-accuracy PacBio correction through iterative short read consensus]]> proovread : large-scale high-accuracy PacBio correction through iterative short read consensus

proovread maps high coverage data to pacbio reads (bwa mem, blasr, daligner) in multiple iterations.

Address of the bookmark: https://github.com/BioInf-Wuerzburg/proovread

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37645/lsc-improving-pacbio-long-read-accuracy-by-short-read-alignment Thu, 06 Sep 2018 16:27:35 -0500 https://bioinformaticsonline.com/bookmarks/view/37645/lsc-improving-pacbio-long-read-accuracy-by-short-read-alignment <![CDATA[LSC: Improving PacBio Long Read Accuracy by Short Read Alignment]]>
  • Added Command line argument support.
  • Multi-stage execution modes.
  • Support for parallelization. Now execution proceeds in batches of long reads the size of which can be set by --long_read_batch_size N.
  • Better compressed intermediate files.
  • Added utilities folder.
  • Added support for multiple short read files.
  • Removed use of configuration file.

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

    ]]>     Abhimanyu Singh     https://bioinformaticsonline.com/bookmarks/view/32131/wgs-celera-assembler-version-83rc2 Mon, 10 Apr 2017 04:45:40 -0500 https://bioinformaticsonline.com/bookmarks/view/32131/wgs-celera-assembler-version-83rc2 <![CDATA[WGS Celera Assembler version 8.3rc2]]> These are release notes for Celera Assembler version 8.3rc2, which was released on May 24, 2015.

    This distribution package provides a stable, tested, documented version of the software.  The distribution is usable on most Unix-like platforms, and some platforms have pre-compiled binary distributions ready for installation.

    The source code package includes full source code (revision 4627), Makefiles, and scripts.  A subset of the kmer package (http://kmer.sourceforge.net/, version r1994), used by some modules of Celera Assembler, is included.  This distribution includes [http://samtools.sourceforge.net/ SAMtools], [http://www.cbcb.umd.edu/software/jellyfish/ Jellyfish 2.0], [https://github.com/pbjd/pbutgcns PBUTGCNS], [https://github.com/PacificBiosciences/pbdagcon PBDAGCON], [https://github.com/PacificBiosciences/BLASR BLASR], and parts of the [https://github.com/PacificBiosciences/FALCON/tree/v0.1.3 Falcon assembler].

    Full documentation can be found online at http://wgs-assembler.sourceforge.net/.

    Interesting scripts within it

    urbe@urbo214b[bin] ls                                                        []
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    -rwxrwxr-x 1 urbe urbe 1,9M Apr 10 11:41 cgw
    -rwxrwxr-x 1 urbe urbe 1,4M Apr 10 11:41 cgwDump
    -rwxrwxr-x 1 urbe urbe 204K Apr 10 11:41 chimChe
    -rwxrwxr-x 1 urbe urbe 201K Apr 10 11:40 chimera
    -rwxrwxr-x 1 urbe urbe 220K Apr 10 11:41 classifyMates
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    -rwxrwxr-x 1 urbe urbe 366K Apr 10 11:41 computeCoverageStat
    -rwxrwxr-x 1 urbe urbe 9,8K Apr 10 11:41 convert-fasta-to-v2.pl
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    -rwxrwxr-x 1 urbe urbe 119K Apr 10 11:41 convertSamToCA
    -rwxrwxr-x 1 urbe urbe  20K Apr 10 11:41 convertToPBCNS
    -rwxrwxr-x 1 urbe urbe 197K Apr 10 11:41 correct-frags
    -rwxrwxr-x 1 urbe urbe 259K Apr 10 11:41 correct-olaps
    -rwxrwxr-x 1 urbe urbe 520K Apr 10 11:41 correctPacBio
    -rwxrwxr-x 1 urbe urbe 540K Apr 10 11:41 ctgcns
    -rwxrwxr-x 1 urbe urbe 162K Apr 10 11:40 deduplicate
    -rwxrwxr-x 1 urbe urbe  37K Apr 10 11:41 demotePosMap
    -rwxrwxr-x 1 urbe urbe 1,5M Apr 10 11:41 dumpCloneMiddles
    -rwxrwxr-x 1 urbe urbe 124K Apr 10 11:41 dumpPBRLayoutStore
    -rwxrwxr-x 1 urbe urbe 1,3M Apr 10 11:41 dumpSingletons
    -rwxrwxr-x 1 urbe urbe 171K Apr 10 11:41 erate-estimate
    -rwxrwxr-x 1 urbe urbe 221K Apr 10 11:40 estimate-mer-threshold
    -rwxrwxr-x 1 urbe urbe 1,5M Apr 10 11:41 extendClearRanges
    -rwxrwxr-x 1 urbe urbe 1,3M Apr 10 11:41 extendClearRangesPartition
    -rwxrwxr-x 1 urbe urbe 205K Apr 10 11:40 extractmessages
    -rwxrwxr-x 1 urbe urbe 7,2M Apr 10 11:41 falcon_sense
    -rwxrwxr-x 1 urbe urbe 9,8K Apr 10 11:41 fastaToCA
    -rwxrwxr-x 1 urbe urbe 124K Apr 10 11:40 fastqAnalyze
    -rwxrwxr-x 1 urbe urbe 137K Apr 10 11:40 fastqSample
    -rwxrwxr-x 1 urbe urbe  62K Apr 10 11:40 fastqSimulate
    -rwxrwxr-x 1 urbe urbe 121K Apr 10 11:40 fastqSimulate-sort
    -rwxrwxr-x 1 urbe urbe 246K Apr 10 11:40 fastqToCA
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    -rwxrwxr-x 1 urbe urbe 341K Apr 10 11:40 finalTrim
    -rwxrwxr-x 1 urbe urbe 228K Apr 10 11:41 fixUnitigs
    -rwxrwxr-x 1 urbe urbe 147K Apr 10 11:40 fragmentDepth
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    -rwxrwxr-x 1 urbe urbe 398K Apr 10 11:40 gatekeeper
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    -rwxrwxr-x 1 urbe urbe 184K Apr 10 11:41 greedyFragmentTiling
    -rwxrwxr-x 1 urbe urbe 1,6K Apr 10 11:41 greedy_layout_to_IUM
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    -rwxrwxr-x 1 urbe urbe 219K Apr 10 11:41 markRepeatUnique
    -rwxrwxr-x 1 urbe urbe 273K Apr 10 11:40 markUniqueUnique
    -rwxrwxr-x 1 urbe urbe 114K Apr 10 11:40 mercy
    -rwxrwxr-x 1 urbe urbe 3,8K Apr 10 11:41 mergeqc.pl
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    -rwxrwxr-x 1 urbe urbe 376K Apr 10 11:40 meryl
    -rwxrwxr-x 1 urbe urbe 176K Apr 10 11:41 metagenomics_ovl_analyses
    -rwxrwxr-x 1 urbe urbe 297K Apr 10 11:41 olap-from-seeds
    -rwxrwxr-x 1 urbe urbe 275K Apr 10 11:41 outputLayout
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    lrwxrwxrwx 1 urbe urbe    4 Apr 10 11:41 pacBioToCA -> PBcR
    -rwxrwxr-x 1 urbe urbe 131K Apr 10 11:41 PBcR
    -rwxrwxr-x 1 urbe urbe 2,9M Apr 10 11:41 pbdagcon
    -rwxrwxr-x 1 urbe urbe 1,9M Apr 10 11:41 pbutgcns
    -rwxrwxr-x 1 urbe urbe 201K Apr 10 11:40 remove_fragment
    -rwxrwxr-x 1 urbe urbe 153K Apr 10 11:40 removeMateOverlap
    -rwxrwxr-x 1 urbe urbe 2,5K Apr 10 11:41 replaceUIDwithName-fastq
    -rwxrwxr-x 1 urbe urbe 1,2K Apr 10 11:41 replaceUIDwithName-posmap
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    -rwxrwxr-x 1 urbe urbe 139K Apr 10 11:41 rewriteCache
    -rwxrwxr-x 1 urbe urbe 232K Apr 10 11:41 runCA
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    -rwxrwxr-x 1 urbe urbe 3,6K Apr 10 11:41 run_greedy.csh
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    -rwxrwxr-x 1 urbe urbe 557K Apr 10 11:41 splitUnitigs
    -rwxrwxr-x 1 urbe urbe 1,4M Apr 10 11:41 terminator
    drwxrwxr-x 2 urbe urbe 4,0K Apr 10 11:41 TIGR
    -rwxrwxr-x 1 urbe urbe 526K Apr 10 11:41 tigStore
    -rwxrwxr-x 1 urbe urbe  35K Apr 10 11:41 tracearchiveToCA
    -rwxrwxr-x 1 urbe urbe  35K Apr 10 11:41 tracedb-to-frg.pl
    -rwxrwxr-x 1 urbe urbe  44K Apr 10 11:41 trimFastqByQVWindow
    -rwxrwxr-x 1 urbe urbe  18K Apr 10 11:40 uidclient
    -rwxrwxr-x 1 urbe urbe 589K Apr 10 11:41 unitigger
    -rwxrwxr-x 1 urbe urbe  42K Apr 10 11:40 upgrade-v8-to-v9
    -rwxrwxr-x 1 urbe urbe  42K Apr 10 11:40 upgrade-v9-to-v10
    -rwxrwxr-x 1 urbe urbe  854 Apr 10 11:41 utg2fasta
    -rwxrwxr-x 1 urbe urbe 731K Apr 10 11:41 utgcns
    -rwxrwxr-x 1 urbe urbe 561K Apr 10 11:41 utgcnsfix


    Address of the bookmark: http://wgs-assembler.sourceforge.net/wiki/index.php/Main_Page

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/39671/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads Sat, 06 Jul 2019 03:48:22 -0500 https://bioinformaticsonline.com/bookmarks/view/39671/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

    ]]>     Rahul Nayak