<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/36478?offset=260 https://bioinformaticsonline.com/bookmarks/view/44491/cgviewjs-is-a-circular-genome-viewing-tool Wed, 27 Mar 2024 11:16:24 -0500 https://bioinformaticsonline.com/bookmarks/view/44491/cgviewjs-is-a-circular-genome-viewing-tool <![CDATA[CGView.js is a Circular Genome Viewing tool]]> CGView.js is a Circular Genome Viewing tool for visualizing and interacting with small genomes. This software is an adaptation of the Java program CGView.

CGView.js is the genome viewer of Proksee, an expert system for genome assembly, annotation and visualization.

Features

  • Circular and linear views of genomes

  • Capable of drawing genomes up to 10 Mbp with 1000's of features and 100's contigs

  • Smooth zooming down to the sequence level

  • Easily generate features and plots directly form the sequence (e.g. ORFs, GC-content and GC-Skew)

  • Save high resolution PNG maps up to 8000x8000px

  • Fully documented API for interacting with CGView.js maps

Address of the bookmark: https://js.cgview.ca/

]]> LEGE https://bioinformaticsonline.com/blog/view/44703/the-role-of-lncrna-in-bioinformatics-unlocking-the-secrets-of-the-genome Sat, 07 Dec 2024 02:09:47 -0600 https://bioinformaticsonline.com/blog/view/44703/the-role-of-lncrna-in-bioinformatics-unlocking-the-secrets-of-the-genome <![CDATA[The Role of lncRNA in Bioinformatics: Unlocking the Secrets of the Genome]]> In the intricate dance of molecular biology, long non-coding RNAs (lncRNAs) have emerged as key players, capturing the interest of researchers worldwide. These RNA molecules, once dismissed as "junk," have proven to be vital in the regulation of gene expression, cellular processes, and the progression of diseases. The intersection of lncRNA studies and bioinformatics is transforming our understanding of these enigmatic molecules, offering profound insights into their structure, function, and therapeutic potential.

What Are lncRNAs?

lncRNAs are RNA transcripts longer than 200 nucleotides that do not code for proteins. Despite their non-coding nature, they play diverse roles in gene regulation, including chromatin remodeling, transcriptional control, and post-transcriptional processing. Unlike messenger RNAs (mRNAs), lncRNAs often function as scaffolds, decoys, or guides in cellular machinery, influencing biological processes such as cell differentiation, immune response, and even cancer metastasis.

Challenges in lncRNA Research

Identifying and understanding lncRNAs pose unique challenges:

  1. High Sequence Variability: Unlike protein-coding genes, lncRNAs exhibit low sequence conservation across species, making functional predictions difficult.
  2. Low Expression Levels: lncRNAs are often expressed at low levels, complicating their detection in transcriptomic data.
  3. Diverse Functions: The multifunctional nature of lncRNAs requires advanced computational tools to decipher their roles in complex networks.

Bioinformatics: A Crucial Ally in lncRNA Research

Bioinformatics bridges the gap between raw biological data and meaningful insights, making it indispensable in lncRNA research. Here’s how:

1. Identification and Annotation

High-throughput sequencing technologies like RNA-seq generate vast amounts of data. Bioinformatics tools such as StringTie, Cufflinks, and HISAT2 help assemble and annotate lncRNAs from this data. Additionally, databases like NONCODE, LNCipedia, and Ensembl provide curated repositories of lncRNA sequences and annotations.

2. Functional Prediction

Bioinformatics algorithms predict the potential functions of lncRNAs by analyzing their interactions with DNA, RNA, and proteins. Tools like LncRNA2Function and RIblast utilize sequence motifs and secondary structure predictions to hypothesize about the roles of specific lncRNAs.

3. Network Construction

lncRNAs often act as regulatory hubs. Bioinformatics platforms such as Cytoscape enable the visualization of lncRNA-mediated networks, elucidating their roles in pathways like cell cycle regulation and apoptosis.

4. Epigenetic Studies

lncRNAs are known to interact with chromatin-modifying complexes, influencing gene expression epigenetically. Tools like ChIP-seq and ATAC-seq, combined with computational pipelines, identify these interactions and map them to the genome.

5. Clinical Applications

Bioinformatics aids in the discovery of lncRNA biomarkers for diseases like cancer and neurodegenerative disorders. Machine learning models analyze differential expression profiles, helping prioritize lncRNAs with therapeutic potential.

Case Study: lncRNAs in Cancer Research

lncRNAs such as HOTAIR and MALAT1 have been implicated in cancer progression. Bioinformatics analyses have revealed their roles in promoting metastasis and altering the tumor microenvironment. For example, transcriptome analysis in cancer patients identifies lncRNA expression signatures, enabling precision medicine approaches.

Future Directions

The fusion of bioinformatics with experimental biology is unlocking the secrets of lncRNAs. Advances in artificial intelligence, single-cell sequencing, and structural modeling promise to overcome current limitations. Here are some promising directions:

  • Integrative Analysis: Combining multi-omics data to understand the interplay of lncRNAs with other biomolecules.
  • CRISPR Screens: Leveraging bioinformatics to design CRISPR-based functional screens for lncRNAs.
  • Therapeutic Development: Using bioinformatics to design lncRNA-based therapeutics, including antisense oligonucleotides and RNA interference tools.

Conclusion

lncRNAs are the hidden gems of the genome, and bioinformatics is the key to unearthing their full potential. As research progresses, lncRNAs could pave the way for novel diagnostics, targeted therapies, and personalized medicine, revolutionizing our approach to complex diseases.

The journey into the world of lncRNAs is only beginning, and bioinformatics will continue to play a pivotal role in decoding these molecular mysteries. Whether you’re a researcher, clinician, or bioinformatics enthusiast, the study of lncRNAs offers a fascinating frontier of discovery.

]]> LEGE https://bioinformaticsonline.com/blog/view/44770/nvidia-and-arc-institute-unveil-evo-2-a-breakthrough-ai-for-dna-design Fri, 21 Feb 2025 10:39:47 -0600 https://bioinformaticsonline.com/blog/view/44770/nvidia-and-arc-institute-unveil-evo-2-a-breakthrough-ai-for-dna-design <![CDATA[NVIDIA and Arc Institute Unveil Evo 2: A Breakthrough AI for DNA Design]]> NVIDIA and the Arc Institute have introduced Evo 2, a groundbreaking AI model designed to understand, predict, and generate DNA sequences. This marks a major advancement in computational biology, offering scientists an unprecedented tool to decode the genetic blueprint of life and even design entirely new biological systems.

The Power of Evo 2: AI Meets DNA

Evo 2 is the largest AI model for biology ever created, trained on an astonishing 9.3 trillion DNA "letters" (nucleotides) carefully selected from genomes spanning the entire tree of life. This massive dataset ensures that Evo 2 can recognize patterns and relationships in genetic sequences at an unparalleled scale.

For the first time, scientists can design DNA with AI, moving beyond simple sequence analysis to active DNA generation. Evo 2 enables researchers to predict, modify, and even create entire genetic sequences, opening new possibilities in medicine, agriculture, and synthetic biology.

Decoding the Dark Genome

One of the biggest challenges in genetics is understanding the non-coding regions of DNA—vast stretches of the genome that do not code for proteins but play crucial roles in regulating gene expression. These regions control when and how genes are activated, influencing everything from development to disease.

Evo 2 is designed to decode these non-coding elements, helping researchers uncover their functions and use this knowledge to develop gene-based therapies, synthetic life forms, and precision agriculture solutions.

From Reading DNA to Writing It

To put Evo 2’s impact into perspective:

  • Previous AI models could "read" DNA like a book, analyzing genetic sequences and identifying patterns.
  • Evo 2 can "write" entirely new DNA, designing functional genes, chromosomes, and even full genomes from scratch.

This means scientists can now engineer biological systems with AI, designing new proteins, metabolic pathways, and genetic circuits to address real-world challenges.

A Step Toward Generative Biology

The Arc Institute describes Evo 2 as a major step toward "generative biology"—a revolutionary approach where AI is used to create novel biological structures rather than just analyzing existing ones. This could lead to breakthroughs such as:

  • New medicines: AI-generated enzymes and proteins tailored for targeted therapies.
  • Disease-resistant crops: Genetically optimized plants for higher yield and climate resilience.
  • Synthetic organisms: Custom-designed microbes for bioremediation, biofuel production, and industrial applications.

An Open-Source Revolution

Unlike many proprietary AI models, Evo 2 is open source, making its capabilities accessible to researchers worldwide. This democratization of AI-driven biology means that scientists from different disciplines can collaborate, experiment, and innovate, accelerating discoveries in genetic engineering and synthetic biology.

With Evo 2, the boundaries of what’s possible in DNA design, genetic engineering, and biological innovation are being redrawn. The future of life sciences is no longer just about understanding life’s code—it’s about writing it.

]]> 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                                                        []
    -rwxrwxr-x 1 urbe urbe  11K Apr 10 11:41 addCNSToStore
    -rwxrwxr-x 1 urbe urbe 575K Apr 10 11:41 addReadsToUnitigs
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    -rwxrwxr-x 1 urbe urbe 1,5M Apr 10 11:41 analyzeScaffolds
    -rwxrwxr-x 1 urbe urbe 224K Apr 10 11:41 asmOutputFasta
    -rwxrwxr-x 1 urbe urbe 448K Apr 10 11:41 asmOutputStatistics
    -rwxrwxr-x 1 urbe urbe 2,4K Apr 10 11:41 asmToAGP.pl
    -rwxrwxr-x 1 urbe urbe 7,6M Apr 10 11:41 blasr
    -rwxrwxr-x 1 urbe urbe 1,6M Apr 10 11:41 bogart
    -rwxrwxr-x 1 urbe urbe 183K Apr 10 11:41 bogus
    -rwxrwxr-x 1 urbe urbe 272K Apr 10 11:41 bogusness
    -rwxrwxr-x 1 urbe urbe 247K Apr 10 11:41 buildPosMap
    -rwxrwxr-x 1 urbe urbe 213K Apr 10 11:41 buildRefContigs
    -rwxrwxr-x 1 urbe urbe 990K Apr 10 11:41 buildUnitigs
    -rwxrwxr-x 1 urbe urbe  18K Apr 10 11:41 ca2ace.pl
    -rwxrwxr-x 1 urbe urbe  12K Apr 10 11:41 caqc_help.ini
    -rwxrwxr-x 1 urbe urbe  61K Apr 10 11:41 caqc.pl
    -rwxrwxr-x 1 urbe urbe  23K Apr 10 11:41 cat-corrects
    -rwxrwxr-x 1 urbe urbe  24K Apr 10 11:41 cat-erates
    -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
    -rwxrwxr-x 1 urbe urbe 201K Apr 10 11:41 classifyMatesApply
    -rwxrwxr-x 1 urbe urbe 215K Apr 10 11:41 classifyMatesPairwise
    -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
    -rwxrwxr-x 1 urbe urbe  48K Apr 10 11:41 convertOverlap
    -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
    -rwxrwxr-x 1 urbe urbe 140K Apr 10 11:41 filterOverlap
    -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
    -rwxrwxr-x 1 urbe urbe  29K Apr 10 11:41 fragsInVars
    -rwxrwxr-x 1 urbe urbe 545K Apr 10 11:41 frgs2clones
    -rwxrwxr-x 1 urbe urbe 398K Apr 10 11:40 gatekeeper
    -rwxrwxr-x 1 urbe urbe 139K Apr 10 11:40 gatekeeperbench
    -rwxrwxr-x 1 urbe urbe 167K Apr 10 11:40 gkpStoreCreate
    -rwxrwxr-x 1 urbe urbe 147K Apr 10 11:40 gkpStoreDumpFASTQ
    -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
    -rwxrwxr-x 1 urbe urbe 142K Apr 10 11:40 initialTrim
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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
    -rwxrwxr-x 1 urbe urbe 422K Apr 10 11:40 merTrim
    -rwxrwxr-x 1 urbe urbe 125K Apr 10 11:40 merTrimApply
    -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
    -rwxrwxr-x 1 urbe urbe 229K Apr 10 11:41 overlapInCore
    -rwxrwxr-x 1 urbe urbe 144K Apr 10 11:40 overlap_partition
    -rwxrwxr-x 1 urbe urbe 179K Apr 10 11:41 overlapStats
    -rwxrwxr-x 1 urbe urbe 179K Apr 10 11:41 overlapStore
    -rwxrwxr-x 1 urbe urbe 153K Apr 10 11:41 overlapStoreBucketizer
    -rwxrwxr-x 1 urbe urbe 175K Apr 10 11:41 overlapStoreBuild
    -rwxrwxr-x 1 urbe urbe  33K Apr 10 11:41 overlapStoreIndexer
    -rwxrwxr-x 1 urbe urbe  48K Apr 10 11:41 overlapStoreSorter
    -rwxrwxr-x 1 urbe urbe 604K Apr 10 11:40 overmerry
    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
    -rwxrwxr-x 1 urbe urbe 1,3M Apr 10 11:41 resolveSurrogates
    -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  14K Apr 10 11:41 runCA-overlapStoreBuild
    -rwxrwxr-x 1 urbe urbe 3,6K Apr 10 11:41 run_greedy.csh
    -rwxrwxr-x 1 urbe urbe 297K Apr 10 11:40 sffToCA
    -rwxrwxr-x 1 urbe urbe  13K Apr 10 11:40 show-corrects
    -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