<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/40604?offset=220 https://bioinformaticsonline.com/pages/view/35802/bioinformatics-tools-to-detect-horizontal-gene-transfer-hgt-in-genomes Fri, 02 Mar 2018 04:56:23 -0600 https://bioinformaticsonline.com/pages/view/35802/bioinformatics-tools-to-detect-horizontal-gene-transfer-hgt-in-genomes <![CDATA[Bioinformatics tools to detect horizontal gene transfer (HGT) in genomes]]> Horizontal gene transfer (HGT), the “non-sexual movement of genetic material between two organisms” , is relatively common in prokaryotes and single-celled eukaryotes, but a number of factors combine to make it far rarer in multicellular eukaryotes. In order for a eukaryotic species to gain a gene by HGT, foreign DNA must enter the host nucleus, integrate into the genome, and in more complex organisms it must enter the sequestered germline in order to be transmitted to offspring. Once there, it must not experience strong negative selection, despite potential for genetic incompatibility with the host genome and mismatch between the niche of the donor and the host. Over the longer term, foreign DNA may become “domesticated” in the recipient genome and provide novel function.

Following are the popular tool to detect HGT in genomes:

T-REX / 3.22

HGT detection / download & compile

20525630

 

RANGER-DTL / 2.0

HGT detection / download binary

22689773

 

PhyloNet / 3.6.1

HGT detection / download binary

18662388

 

Jane / 4.01

HGT detection / download binary (!license!)

20181081

 

TREE-PUZZLE / 5.3.rc16

HGT detection / download & compile

11934758

 

CONSEL / 0.20

HGT detection / download

11751242

 

DarkHorse / 1.5 rev170

HGT detection / download & install

17274820

 

HGTector / 0.2.1

HGT detection / git clone

25159222

 

EGID / 1.0

HGT detection / download

22355228

 

GeneMarkS / 4.30

HGT detection / download binary (!license!)

9461475

]]> Jit https://bioinformaticsonline.com/pages/view/36392/protein-protein-interaction-sites-predictions Wed, 25 Apr 2018 04:53:20 -0500 https://bioinformaticsonline.com/pages/view/36392/protein-protein-interaction-sites-predictions <![CDATA[Protein-Protein Interaction Sites Predictions !]]> The study of Protein–Protein Interactions (PPIs) has a crucial role in biology, medicine and the pharmaceutical industry. PPIs can be investigated from two aspects: The interaction partners of a specific protein and the amino acid residues participating in a given PPI. Information about a protein’s interaction partners allows scientists to construct protein interaction networks, such as signaling pathways, which in turn facilitate the understanding of many biological and clinical observations. 

Following are the list of tools commonly used to PPIs predictions:

Protein-Protein Interaction Sites

PPISP

A consensus neural network method for predicting protein-protein interaction sites

HOMCOS

A server to predict interacting protein pairs and interacting sites by homology modeling of complex structures

HotPOINT

Prediction of protein interfaces using an empirical model

ISIS

Prediction of interaction hotspots from sequence

KFC server

Automated decision-tree approach to predicting protein-protein interaction hot spots

meta-PPISP

A meta server for predicting protein-protein interaction sites. meta-PPISP is built on three individual web servers: cons-PPISP, PINUP, and Promate

ODA

Identification of optimal surface patches with the lowest docking desolvation energy values

PINUP

Protein binding site prediction with an empirical scoring function

Other Sites (DNA, RNA, Metals)

CHED 

Web server for predicting soft metal binding sites in proteins

DBD-Hunter

A knowledge-based method for the prediction of DNA-protein interactions

DISPLAR

Given the structure of a protein known to bind DNA, the method predicts residues that contact DNA using neural network method

iDBPs

Predicts DNA binding proteins for proteins with known 3D structure.

PFplus

A tool for extracting and displaying positive electrostatic patches on protein surfaces which can be indicative of nucleic acid binding interfaces.
]]> Poonam Mahapatra https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping Tue, 08 May 2018 04:15:25 -0500 https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping <![CDATA[MITObim - mitochondrial baiting and iterative mapping]]> This document contains instructions on how to use the MITObim pipeline described in Hahn et al. 2013. The full article can be found here. Kindly cite the article if you are using MITObim in your work. The pipeline was originally developed for Illumina data, but thanks to the versatility of the MIRA assembler, MITObim supports in principle also data from the Iontorrent, 454 and PacBio sequencing platforms.

Below you can find a few basic tutorials for how to run MITObim and I encorage you to give them a try with the testdata that comes with this Repo, just to make sure everything is running smoothly on your system. It'll only take a few minutes and will potentially safe you a lot of time down the line.

I provide further examples here as Jupyter notebooks. Get in touch if you feel like sharing your particular MITObim solution and I'd be happy to put it up here, too!

Address of the bookmark: https://github.com/chrishah/MITObim

]]> Rahul Nayak https://bioinformaticsonline.com/blog/view/36525/installing-bandage-on-ubunty Tue, 08 May 2018 08:03:21 -0500 https://bioinformaticsonline.com/blog/view/36525/installing-bandage-on-ubunty <![CDATA[Installing Bandage on Ubunty !]]> The following instructions successfully build Bandage (https://github.com/rrwick/Bandage ) on a fresh installation of Ubuntu 14.04:

  1. Ensure the package lists are up-to-date: sudo apt-get update
  2. Install prerequisite packages: sudo apt-get install build-essential git qtbase5-dev libqt5svg5-dev
  3. Download the Bandage code from GitHub: git clone https://github.com/rrwick/Bandage.git
  4. Open a terminal in the Bandage directory.
  5. Set the environment variable to specify that you will be using Qt 5, not Qt 4: export QT_SELECT=5
  6. Run qmake to generate a Makefile: qmake
  7. Build the program: make
  8. Bandage should now be an executable file.
  9. Optionally, copy the program into /usr/local/bin: sudo make install. The Bandage build directory can then be deleted.

➜ Tools git:(master) ✗ sudo apt-get update
[sudo] password for urbe: 
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➜ Tools git:(master) ✗ sudo apt-get install build-essential git qtbase5-dev libqt5svg5-dev
Reading package lists... Done
Building dependency tree 
Reading state information... Done
build-essential is already the newest version (12.1ubuntu2).
git is already the newest version (1:2.7.4-0ubuntu1.3).
The following packages were automatically installed and are no longer required:
bridge-utils containerd linux-headers-4.4.0-116 linux-headers-4.4.0-116-generic linux-headers-4.4.0-21 linux-headers-4.4.0-21-generic linux-image-4.4.0-116-generic linux-image-4.4.0-21-generic
linux-image-extra-4.4.0-116-generic linux-image-extra-4.4.0-21-generic linux-signed-image-4.4.0-116-generic runc ubuntu-fan
Use 'sudo apt autoremove' to remove them.
The following additional packages will be installed:
libdrm-dev libegl1-mesa-dev libgl1-mesa-dev libgles2-mesa libgles2-mesa-dev libglu1-mesa-dev libmirclient-dev libmircommon-dev libmircookie-dev libmircookie2 libmircore-dev libprotobuf-dev libprotobuf9v5
libqt5concurrent5 libqt5core5a libqt5dbus5 libqt5gui5 libqt5network5 libqt5opengl5 libqt5opengl5-dev libqt5printsupport5 libqt5sql5 libqt5sql5-sqlite libqt5svg5 libqt5test5 libqt5widgets5 libqt5xml5 libwayland-bin
libwayland-dev libx11-xcb-dev libxcb-dri2-0-dev libxcb-dri3-dev libxcb-glx0-dev libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-present-dev libxcb-randr0 libxcb-randr0-dev libxcb-render-util0 libxcb-render0-dev
libxcb-shape0-dev libxcb-sync-dev libxcb-xfixes0-dev libxcb-xkb1 libxdamage-dev libxext-dev libxfixes-dev libxkbcommon-dev libxkbcommon-x11-0 libxshmfence-dev libxxf86vm-dev mesa-common-dev qt5-qmake
qtbase5-dev-tools qttranslations5-l10n x11proto-damage-dev x11proto-dri2-dev x11proto-fixes-dev x11proto-gl-dev x11proto-xext-dev x11proto-xf86vidmode-dev
Suggested packages:
libqt5libqgtk2 qt5-image-formats-plugins qtwayland5 libxext-doc libmysqlclient-dev libpq-dev libsqlite3-dev unixodbc-dev
The following NEW packages will be installed:
libdrm-dev libegl1-mesa-dev libgl1-mesa-dev libgles2-mesa libgles2-mesa-dev libglu1-mesa-dev libmirclient-dev libmircommon-dev libmircookie-dev libmircookie2 libmircore-dev libprotobuf-dev libprotobuf9v5
libqt5concurrent5 libqt5core5a libqt5dbus5 libqt5gui5 libqt5network5 libqt5opengl5 libqt5opengl5-dev libqt5printsupport5 libqt5sql5 libqt5sql5-sqlite libqt5svg5 libqt5svg5-dev libqt5test5 libqt5widgets5 libqt5xml5
libwayland-bin libwayland-dev libx11-xcb-dev libxcb-dri2-0-dev libxcb-dri3-dev libxcb-glx0-dev libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-present-dev libxcb-randr0 libxcb-randr0-dev libxcb-render-util0
libxcb-render0-dev libxcb-shape0-dev libxcb-sync-dev libxcb-xfixes0-dev libxcb-xkb1 libxdamage-dev libxext-dev libxfixes-dev libxkbcommon-dev libxkbcommon-x11-0 libxshmfence-dev libxxf86vm-dev mesa-common-dev
qt5-qmake qtbase5-dev qtbase5-dev-tools qttranslations5-l10n x11proto-damage-dev x11proto-dri2-dev x11proto-fixes-dev x11proto-gl-dev x11proto-xext-dev x11proto-xf86vidmode-dev
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➜ Tools git:(master) ✗ git clone https://github.com/rrwick/Bandage.git
Cloning into 'Bandage'...
remote: Counting objects: 7813, done.
remote: Total 7813 (delta 0), reused 0 (delta 0), pack-reused 7813
Receiving objects: 100% (7813/7813), 27.43 MiB | 16.33 MiB/s, done.
Resolving deltas: 100% (5973/5973), done.
Checking connectivity... done.
➜ Tools git:(master) ✗ cd Bandage 
➜ Bandage git:(master) ls
Bandage.pro BandageTests.pro blast build_scripts command_line COPYING graph images ogdf program README.md tests ui
➜ Bandage git:(master) export QT_SELECT=5
➜ Bandage git:(master) qmake
➜ Bandage git:(master) ✗ make
/home/urbe/anaconda3/bin/uic ui/mainwindow.ui -o ui_mainwindow.h
/home/urbe/anaconda3/bin/uic ui/settingsdialog.ui -o ui_settingsdialog.h
/home/urbe/anaconda3/bin/uic ui/aboutdialog.ui -o ui_aboutdialog.h
/home/urbe/anaconda3/bin/uic ui/enteroneblastquerydialog.ui -o ui_enteroneblastquerydialog.h
/home/urbe/anaconda3/bin/uic ui/blastsearchdialog.ui -o ui_blastsearchdialog.h
/home/urbe/anaconda3/bin/uic ui/myprogressdialog.ui -o ui_myprogressdialog.h
/home/urbe/anaconda3/bin/uic ui/pathspecifydialog.ui -o ui_pathspecifydialog.h
/home/urbe/anaconda3/bin/uic ui/querypathsdialog.ui -o ui_querypathsdialog.h
/home/urbe/anaconda3/bin/uic ui/blasthitfiltersdialog.ui -o ui_blasthitfiltersdialog.h
/home/urbe/anaconda3/bin/uic ui/changenodenamedialog.ui -o ui_changenodenamedialog.h
/home/urbe/anaconda3/bin/uic ui/graphinfodialog.ui -o ui_graphinfodialog.h
/home/urbe/anaconda3/bin/uic ui/changenodedepthdialog.ui -o ui_changenodedepthdialog.h
g++ -c -pipe -O2 -std=gnu++0x -Wall -W -D_REENTRANT -fPIC -DQT_NO_DEBUG -DQT_SVG_LIB -DQT_WIDGETS_LIB -DQT_GUI_LIB -DQT_CORE_LIB -I. -Iui -I/usr/include -I../../anaconda3/include/qt -I../../anaconda3/include/qt/QtSvg -I../../anaconda3/include/qt/QtWidgets -I../../anaconda3/include/qt/QtGui -I../../anaconda3/include/qt/QtCore -I. -I. -I../../anaconda3/mkspecs/linux-g++ -o main.o program/main.cpp
g++ -c -pipe -O2 -std=gnu++0x -Wall -W -D_REENTRANT -fPIC -DQT_NO_DEBUG -DQT_SVG_LIB -DQT_WIDGETS_LIB -DQT_GUI_LIB -DQT_CORE_LIB -I. -Iui -I/usr/include -I../../anaconda3/include/qt -I../../anaconda3/include/qt/QtSvg -I../../anaconda3/include/qt/QtWidgets -I../../anaconda3/include/qt/QtGui -I../../anaconda3/include/qt/QtCore -I. -I. -I../../anaconda3/mkspecs/linux-g++ -o settings.o program/settings.cpp
....

...
g++ -Wl,-O1 -Wl,-rpath,/home/urbe/anaconda3/lib -o Bandage main.o settings.o globals.o graphlayoutworker.o debruijnnode.o debruijnedge.o graphicsitemnode.o graphicsitemedge.o mainwindow.o graphicsviewzoom.o settingsdialog.o mygraphicsview.o mygraphicsscene.o aboutdialog.o enteroneblastquerydialog.o blasthit.o blastqueries.o blastsearchdialog.o infotextwidget.o assemblygraph.o verticalscrollarea.o myprogressdialog.o nodewidthvisualaid.o verticallabel.o load.o image.o commoncommandlinefunctions.o mytablewidget.o buildblastdatabaseworker.o colourbutton.o blastquery.o runblastsearchworker.o blastsearch.o path.o pathspecifydialog.o graphlocation.o tablewidgetitemint.o tablewidgetitemdouble.o tablewidgetitemshown.o memory.o querypathspushbutton.o querypathsdialog.o blastquerypath.o blasthitfiltersdialog.o scinot.o changenodenamedialog.o querypathsequencecopybutton.o querypaths.o info.o reduce.o Graph.o GraphAttributes.o FMMMLayout.o geometry.o ClusterGraphAttributes.o FruchtermanReingold.o NMM.o GmlParser.o simple_graph_alg.o basic.o XmlParser.o String.o Hashing.o PoolMemoryAllocator.o GraphCopy.o CombinatorialEmbedding.o OgmlParser.o ClusterGraph.o Math.o EdgeAttributes.o NodeAttributes.o MAARPacking.o Multilevel.o numexcept.o Set.o Ogml.o DinoXmlParser.o DinoXmlScanner.o DinoTools.o DinoLineBuffer.o System.o QuadTreeNM.o QuadTreeNodeNM.o Constraint.o MultilevelGraph.o graphinfodialog.o tablewidgetitemname.o changenodedepthdialog.o qrc_images.o moc_graphlayoutworker.o moc_mainwindow.o moc_graphicsviewzoom.o moc_settingsdialog.o moc_mygraphicsview.o moc_mygraphicsscene.o moc_aboutdialog.o moc_enteroneblastquerydialog.o moc_blastquery.o moc_blastsearchdialog.o moc_infotextwidget.o moc_assemblygraph.o moc_verticalscrollarea.o moc_myprogressdialog.o moc_nodewidthvisualaid.o moc_verticallabel.o moc_mytablewidget.o moc_buildblastdatabaseworker.o moc_colourbutton.o moc_runblastsearchworker.o moc_pathspecifydialog.o moc_querypathspushbutton.o moc_querypathsdialog.o moc_blasthitfiltersdialog.o moc_changenodenamedialog.o moc_querypathsequencecopybutton.o moc_graphinfodialog.o moc_changenodedepthdialog.o -L/usr/lib -L/home/urbe/anaconda3/lib -lQt5Svg -lQt5Widgets -lQt5Gui -lQt5Core -lGL -lpthread 
➜ Bandage git:(master) ✗ ls 
aboutdialog.o DinoTools.o Makefile moc_infotextwidget.cpp moc_verticalscrollarea.o scinot.o
assemblygraph.o DinoXmlParser.o Math.o moc_infotextwidget.o MultilevelGraph.o Set.o
Bandage DinoXmlScanner.o memory.o moc_mainwindow.cpp Multilevel.o settingsdialog.o
Bandage.pro EdgeAttributes.o moc_aboutdialog.cpp moc_mainwindow.o mygraphicsscene.o settings.o
BandageTests.pro enteroneblastquerydialog.o moc_aboutdialog.o moc_mygraphicsscene.cpp mygraphicsview.o simple_graph_alg.o
basic.o FMMMLayout.o moc_assemblygraph.cpp moc_mygraphicsscene.o myprogressdialog.o String.o
blast FruchtermanReingold.o moc_assemblygraph.o moc_mygraphicsview.cpp mytablewidget.o System.o
blasthitfiltersdialog.o geometry.o moc_blasthitfiltersdialog.cpp moc_mygraphicsview.o NMM.o tablewidgetitemdouble.o
blasthit.o globals.o moc_blasthitfiltersdialog.o moc_myprogressdialog.cpp NodeAttributes.o tablewidgetitemint.o
blastqueries.o GmlParser.o moc_blastquery.cpp moc_myprogressdialog.o nodewidthvisualaid.o tablewidgetitemname.o
blastquery.o graph moc_blastquery.o moc_mytablewidget.cpp numexcept.o tablewidgetitemshown.o
blastquerypath.o GraphAttributes.o moc_blastsearchdialog.cpp moc_mytablewidget.o ogdf tests
blastsearchdialog.o GraphCopy.o moc_blastsearchdialog.o moc_nodewidthvisualaid.cpp Ogml.o ui
blastsearch.o graphicsitemedge.o moc_buildblastdatabaseworker.cpp moc_nodewidthvisualaid.o OgmlParser.o ui_aboutdialog.h
buildblastdatabaseworker.o graphicsitemnode.o moc_buildblastdatabaseworker.o moc_pathspecifydialog.cpp path.o ui_blasthitfiltersdialog.h
build_scripts graphicsviewzoom.o moc_changenodedepthdialog.cpp moc_pathspecifydialog.o pathspecifydialog.o ui_blastsearchdialog.h
changenodedepthdialog.o graphinfodialog.o moc_changenodedepthdialog.o moc_querypathsdialog.cpp PoolMemoryAllocator.o ui_changenodedepthdialog.h
changenodenamedialog.o graphlayoutworker.o moc_changenodenamedialog.cpp moc_querypathsdialog.o program ui_changenodenamedialog.h
ClusterGraphAttributes.o graphlocation.o moc_changenodenamedialog.o moc_querypathsequencecopybutton.cpp qrc_images.cpp ui_enteroneblastquerydialog.h
ClusterGraph.o Graph.o moc_colourbutton.cpp moc_querypathsequencecopybutton.o qrc_images.o ui_graphinfodialog.h
colourbutton.o Hashing.o moc_colourbutton.o moc_querypathspushbutton.cpp QuadTreeNM.o ui_mainwindow.h
CombinatorialEmbedding.o image.o moc_enteroneblastquerydialog.cpp moc_querypathspushbutton.o QuadTreeNodeNM.o ui_myprogressdialog.h
command_line images moc_enteroneblastquerydialog.o moc_runblastsearchworker.cpp querypathsdialog.o ui_pathspecifydialog.h
commoncommandlinefunctions.o info.o moc_graphicsviewzoom.cpp moc_runblastsearchworker.o querypathsequencecopybutton.o ui_querypathsdialog.h
Constraint.o infotextwidget.o moc_graphicsviewzoom.o moc_settingsdialog.cpp querypaths.o ui_settingsdialog.h
COPYING load.o moc_graphinfodialog.cpp moc_settingsdialog.o querypathspushbutton.o verticallabel.o
debruijnedge.o MAARPacking.o moc_graphinfodialog.o moc_verticallabel.cpp README.md verticalscrollarea.o
debruijnnode.o main.o moc_graphlayoutworker.cpp moc_verticallabel.o reduce.o XmlParser.o
DinoLineBuffer.o mainwindow.o moc_graphlayoutworker.o moc_verticalscrollarea.cpp runblastsearchworker.o
➜ Bandage git:(master) ✗ ./Bandage

]]> Jit https://bioinformaticsonline.com/blog/view/43670/useful-bioinformatics-analysis-tools Thu, 23 Dec 2021 23:10:02 -0600 https://bioinformaticsonline.com/blog/view/43670/useful-bioinformatics-analysis-tools <![CDATA[Useful Bioinformatics Analysis Tools !]]> CoMeta

Classificier of reads from metagenomic sequencing experiments.

•  Kawulok, J., Deorowicz, S., CoMeta: Classification of Metagenomes Using k-mers, PLOS ONE, 2015; 10(4):1–23,

CoMSA

Compressor of multiple sequence alignments of proteins.

•  Deorowicz, S., Walczyszyn, J., Debudaj-Grabysz, A., CoMSA: compression of protein multiple sequence alignment files, Bioinformatics, 2019; 35(2):22–234,

DSRC

Compressor of sequencing reads.

•  Roguski, L., Deorowicz, S., DSRC 2: Industry-oriented compression of FASTQ files, Bioinformatics, 2014; 30(15):2213–2215,
•  Deorowicz, S., Grabowski, Sz., Compression of DNA sequences in FASTQ format, Bioinformatics, 2011; 27(6):860–862,

FAMSA

Multiple sequence alignment designed for huge families of proteins (even containing hundreds of thousands of sequences).

•  Deorowicz, S., Debudaj-Grabysz, A., Gudys, A., FAMSA: Fast and accurate multiple sequence alignment of huge protein families, Scientific Reports, 2016; 6(33964):

FaStore

Compressor of FASTQ files.

•  Roguski, L., Ochoa, I., Hernaez, M., Deorowicz, S., FaStore - a space-saving solution for raw sequencing data, Bioinformatics, 2018; 34(16):2748–2756,

FQSqueezer

Experimental high-end compressor of FASTQ files.

•  Deorowicz, S., FQSqueezer: k-mer-based compression of sequencing data, Scientific Reports, 2020; 10(578):

GDC

Compressor of collections of genome sequences.

•  Deorowicz, S., Danek, A., Niemiec, M., GDC 2: Compression of large collections of genomes, Scientific Reports, 2015; 5(11565):1–12,
•  Deorowicz, S., Grabowski, Sz., Robust relative compression of genomes with random access, Bioinformatics, 2011; 27(21):2979–2986,

GTC

Genotype databases compressor with support for fast queries.

•  Danek, A., Deorowicz, S., GTC: how to maintain huge genotype collections in a compressed form, Bioinformatics, 2018; 34(11):1834–1840,

GTShark

Genotypes compressor.

•  Deorowicz, S., Danek, A., GTShark: Genotype compression in large projects, Bioinformatics, 2019; 35(22):4791–4793,

KMC

Memory frugal k-mer counter.

•  Kokot, M., Długosz, M., Deorowicz, S., KMC 3: counting and manipulating k -mer statistics, Bioinformatics, 2017; 33(17):2759–2761,
•  Deorowicz, S., Kokot, M., Grabowski, Sz., Debudaj-Grabysz, A., KMC 2: Fast and resource-frugal k-mer counting, Bioinformatics, 2015; 31(10):1569–1576,
•  Deorowicz, S., Debudaj-Grabysz, A., Grabowski, Sz., Disk-based k-mer counting on a PC, BMC Bioinformatics, 2013; 14():Article no. 160,

Kmer-db

Tool for estimation of evolutionary distances in a collection of genomes.

•  Deorowicz, S., Gudys, A., Dlugosz, M., Kokot, M., Danek, A., Kmer-db: instant evolutionary distance estimation, Bioinformatics, 2019; 35(1):133–136,

MuGI

Index allowing queries for a collection of multiple genome sequences.

•  Danek, A., Deorowicz, S., Grabowski, Sz., Indexes of Large Genome Collections on a PC, PLOS ONE, 2014; 9(10):e109384,

ORCOM

Experimental compressor of sequencing reads.

•  Grabowski, Sz., Deorowicz, S., Roguski, L., Disk-based compression of data from genome sequencing, Bioinformatics, 2014; 31(9):1389–1395,

PgSA

Index allowing queries for a collection of sequencing reads.

•  Kowalski, T., Grabowski, Sz., Deorowicz, S., Indexing arbitrary-length k-mers in sequencing reads, PLOS ONE, 2015; 10(7):1–16,

QuickProbs

Multiple sequence alignment designed especially for GPU.

•  Gudys, A., Deorowicz, S., QuickProbs 2: towards rapid construction of high-quality alignments of large protein families, Scientific Reports, 2017; 7(41553):
•  Gudys, A., Deorowicz, S., QuickProbs – A Fast Multiple Sequence Alignment Algorithm Designed for Graphics Processors, PLOS ONE, 2014; 9(2):e88901,

RECKONER

Read error corrector.

•  Maciej Długosz, M., Deorowicz, S., RECKONER: read error corrector based on KMC, Bioinformatics, 2017; 33(7):1086–1089,

TGC

Compressor of collections of genomes given in Variant Call Format (VCF) files.

•  Deorowicz, S., Danek, A., Grabowski, Sz., Genome compression: a novel approach for large collections, Bioinformatics, 2013; 29(20):2572–2578,

VCFShark

Compressor of VCF files.

•  Deorowicz, S., Danek, A., GTShark: Genotype compression in large projects, biorxiv.org, 2020; ():

Whisper

Experimental mapper of whole genome sequencing data.

•  Deorowicz, S., Gudys, A., Whisper 2: indel-sensitive short read mapping, bioRxiv.org, 2019; :
•  Deorowicz, S., Debudaj-Grabysz, A., Gudys, A., Grabowski, Sz., Whisper: read sorting allows robust robust mapping of DNA sequencing data, Bioinformatics, 2019; 35(12):2043–2050,
•  Deorowicz, S., Debudaj-Grabysz, A., Gudys, A., Grabowski, Sz., Robust mapping of whole genome sequencing data, Poster at The Biology of Genomes Conference, 2017;

]]> Neel https://bioinformaticsonline.com/blog/view/44002/interesting-bioinformatics-resources Fri, 11 Nov 2022 06:30:46 -0600 https://bioinformaticsonline.com/blog/view/44002/interesting-bioinformatics-resources <![CDATA[Interesting Bioinformatics Resources !]]> 1. a reproducible workflow. https://www.youtube.com/watch?v=s3JldKoA0zw This two minute video will change your mind on reproducible research 

2. Parallel sequencing lives, or what makes large sequencing projects successful https://academic.oup.com/gigascience/article/6/11/gix100/4557140?login=false

3. Common-sense approaches to sharing tabular data alongside publication https://www.sciencedirect.com/science/article/pii/S2666389921002300

4. A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker https://psyarxiv.com/8xzqy/

5. Practical Computational Reproducibility in the Life Sciences https://www.cell.com/cell-systems/fulltext/S2405-4712(18)30140-6

6. A video by Dr.Keith A. Baggerly from MD Anderson [The Importance of Reproducible Research in High-Throughput Biology](https://www.youtube.com/watch?v=7gYIs7uYbMo) highly recommended.

7. Ten Simple Rules for Reproducible Computational Research http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003285)

8. Good Enough Practices in Scientific Computing http://arxiv.org/abs/1609.00037 

9. Best Practices for Scientific Computing https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745

10. A Quick Guide to Organizing Computational Biology Projects http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.100042  A must read for computational biologists!

11. Reproducibility of computational workflows is automated using continuous analysis https://www.nature.com/articles/nbt.3780

12. Five selfish reasons to work reproducibly https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0850-7

]]> Abhi https://bioinformaticsonline.com/pages/view/44618/important-bioinformatics-tools Tue, 30 Jul 2024 05:03:29 -0500 https://bioinformaticsonline.com/pages/view/44618/important-bioinformatics-tools <![CDATA[Important Bioinformatics Tools !]]> 1. Ktrim: An extra-fast, accurate adapter trimmer for sequencing data. It processes FASTQ files from multiple lanes with minimal mismatching and over-trimming of adapters.

2. BWA MEM: A reliable alignment tool (particularly for mapping ALT contigs and HLA genes, which are not fully addressed in BWA-MEM2).

3. Sambamba markdup: Quickly marks or removes duplicate reads using Picard's criteria.

4. ichorCNA: Estimates the tumor DNA fraction in cell-free DNA from ultra-low-pass whole genome sequencing (0.1x coverage) based on copy number alterations (CNA).

5. Fragle: A deep learning method for quantifying ctDNA levels from cell-free DNA fragmentomic profiles. It detects TF as low as ~1% ctDNA and works with targeted genomic panel sequencing data.

6. AlfredQC: A quality control tool for high-throughput sequencing data. It assesses metrics like read quality scores, GC content, and duplication rates, visualized through detailed plots and summary statistics.

7. Mosdepth: A fast tool for calculating sequencing coverage depth, offering a quicker alternative to samtools/sambamba depth by processing BAM and CRAM files.

8. Bedtools: A versatile toolkit for genomics, enabling operations like intersect, merge, count, and shuffle on genomic intervals across formats such as BAM, BED, GFF/GTF, and VCF.

9. Datamash: A command-line tool for basic numeric, textual, and statistical operations on input data streams. It supports operations such as grouping, sorting, transposing, and performing arithmetic calculations on tabular data.

10. gwf.app: A pragmatic alternative to Snakemake. Developed at Aarhus University, this flexible, generic workflow tool builds and runs large scientific workflows.

]]> BioStar https://bioinformaticsonline.com/blog/view/44914/predicting-pathogen-virulence-using-bioinformatics-tools Tue, 04 Nov 2025 07:55:53 -0600 https://bioinformaticsonline.com/blog/view/44914/predicting-pathogen-virulence-using-bioinformatics-tools <![CDATA[Predicting Pathogen Virulence Using Bioinformatics Tools]]> In the genomic era, the ability to predict the virulence potential of pathogens has become an indispensable part of infectious disease research. With the exponential growth of microbial genome data, bioinformatics tools now enable scientists to identify virulence factors, model pathogen behavior, and even forecast outbreak risks — all from sequence data.

In an age where pathogens continue to evolve and cross boundaries, understanding what makes them virulent—that is, capable of causing disease—has become a critical focus in modern microbiology and genomics. Virulence prediction bridges computational biology, genomics, and machine learning to forecast the pathogenic potential of microbes before they strike.

What Is Virulence?

Virulence refers to the degree of damage a pathogen can inflict on its host. It is determined by a combination of genetic factors—called virulence factors (VFs)—that allow the organism to attach, invade, evade, and harm the host. These include genes coding for toxins, secretion systems, adhesins, and enzymes that disrupt host defenses.

Understanding virulence factors not only helps in deciphering the mechanisms of infection but also provides early warning signs for emerging threats.

Why Predict Virulence?

Traditional virulence studies relied heavily on experimental infection models, which, although accurate, are time-consuming, expensive, and ethically constrained.
Today, the availability of whole-genome sequences and large-scale pathogen databases has paved the way for in silico virulence prediction—a computational approach that can screen thousands of genomes within hours.

This approach enables researchers to:

  • Rapidly identify potential high-risk strains.

  • Prioritize pathogens for containment, surveillance, or further study.

  • Guide vaccine development and drug target discovery.

  • Support One Health frameworks, linking animal, human, and environmental health data.

How Is Virulence Predicted?

Virulence prediction combines bioinformatics pipelines with machine learning and comparative genomics. The process generally involves:

  1. Genome Annotation: Identifying genes and coding sequences in microbial genomes.

  2. Feature Extraction: Comparing sequences with curated databases like VFDB (Virulence Factor Database), PATRIC, or Victors.

  3. Pattern Recognition: Using algorithms (e.g., Random Forest, SVM, or deep learning models) to classify genes or strains as virulent or non-virulent based on sequence patterns, motifs, and protein domains.

  4. Scoring and Visualization: Assigning a virulence score or confidence level and visualizing it through heatmaps or genome maps.

Tools and Resources for Virulence Prediction

A number of tools and databases make virulence prediction accessible to the scientific community:

  • VFanalyzer – For identifying virulence genes based on VFDB.

  • PathoFact – Predicts virulence, antimicrobial resistance (AMR), and toxin genes from metagenomic data.

  • Pangenome-based models – Identify virulence-associated gene clusters across strains.

  • Machine learning models – Use features like GC content, codon usage bias, or protein domains to predict pathogenicity.

Emerging tools now integrate multi-omic data—including transcriptomics, proteomics, and metabolomics—to understand virulence in a systems biology framework.

Applications in the Real World

Virulence prediction has major implications across public health and research sectors:

  • Epidemic preparedness: Early identification of virulent strains in outbreak samples.

  • AMR surveillance: Linking virulence profiles with antibiotic resistance determinants.

  • Environmental monitoring: Predicting pathogenic potential of soil or waterborne microbes.

  • Clinical diagnostics: Supporting personalized treatment through pathogen profiling.

For instance, integrating virulence prediction pipelines into national surveillance networks could enable faster risk assessment and response to infectious outbreaks.

The Road Ahead

As machine learning and genomics advance, virulence prediction will evolve from simple gene-based detection to dynamic, context-aware models that account for host–pathogen interactions, environmental signals, and evolutionary adaptation.

Future tools may predict not just if a strain is virulent, but under what conditions it expresses that virulence—bridging the gap between genotype and phenotype.

In Summary

Virulence prediction is redefining how we understand and anticipate infectious diseases. By coupling genomic insights with computational intelligence, researchers can identify potential threats earlier, design smarter interventions, and ultimately, strengthen our preparedness against emerging pathogens.

]]> BioStar https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis Tue, 07 Nov 2017 05:33:33 -0600 https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis <![CDATA[Alignment-free sequence comparison tools available for next-generation sequencing data analysis]]>

kallisto

Transcript abundance quantification from RNA-seq data (uses pseudoalignment for rapid determination of read compatibility with targets)

Software (C++)

https://pachterlab.github.io/kallisto/

Sailfish

Estimation of isoform abundances from reference sequences and RNA-seq data (k-mer based)

Software (C++)

http://www.cs.cmu.edu/~ckingsf/software/sailfish/

Salmon

Quantification of the expression of transcripts using RNA-seq data (uses k-mers)

https://combine-lab.github.io/salmon/

RNA-Skim

RNA-seq quantification at transcript-level (partitions the transcriptome into disjoint transcript clusters; uses sig-mers, a special type of k-mers)

Software (C++)

http://www.csbio.unc.edu/rs/

Variant calling

ChimeRScope

Fusion transcript prediction using gene k-mers profiles of the RNA-seq paired-end reads

Software (Java)

https://github.com/ChimeRScope/ChimeRScope/wiki

FastGT

Genotyping of known SNV/SNP variants directly from raw NGS sequence reads by counting unique k-mers

Software (C)

https://github.com/bioinfo-ut/GenomeTester4/

Phy-Mer

Reference-independent mitochondrial haplogroup classifier from NGS data (k-mer based)

Software (Python)

https://github.com/danielnavarrogomez/phy-mer

LAVA

Genotyping of known SNPs (dbSNP and Affymetrix's Genome-Wide Human SNP Array) from raw NGS reads (k-mer based)

Software (C)

http://lava.csail.mit.edu/

MICADo

Detection of mutations in targeted third-generation NGS data (can distinguish patients’ specific mutations; algorithm uses k-mers and is based on colored de Bruijn graphs)

Software (Python)

http://github.com/cbib/MICADo

General mapper

Minimap

Lightweight and fast read mapper and read overlap detector (uses the concept of “minimazers”, a special type of k-mers)

Software (C)

https://github.com/lh3/minimap

Assembly

De novo genome assembly

MHAP

Produces highly continuous assembly (fully resolved chromosome arms) from third-generation long and noisy reads (10 kbp) using a dimensionality reduction technique MinHash

Software (Java)

https://github.com/marbl/MHAP

Miniasm

Assembler of long noisy reads (SMRT, ONT) using the Overlap-Layout Consensus (OLC) approach without the necessity of an error correction stage (uses minimap)

Software (C)

https://github.com/lh3/miniasm

LINKS

Scaffolding genome assembly with error-containing long sequence (e.g., ONT or PacBio reads, draft genomes)

Software (Perl)

https://github.com/warrenlr/LINKS/

Read clustering

afcluster

Clustering of reads from different genes and different species based on k-mer counts

Software (C++)

https://github.com/luscinius/afcluster

QCluster

Clustering of reads with alignment-free measures (k-mer based) and quality values

Software (C++)

http://www.dei.unipd.it/~ciompin/main/qcluster.html

Reads error correction

Lighter

Correction of sequencing errors in raw, whole genome sequencing reads (k-mer based)

Software (C++)

https://github.com/mourisl/Lighter

QuorUM

Error corrector for Illumina reads using k-mers

Software (C++)

https://github.com/gmarcais/Quorum

Trowel

Software (C++)

https://sourceforge.net/projects/trowel-ec/

Metagenomics

Assembly-free phylogenomics

AAF

Phylogeny reconstruction directly from unassembled raw sequence data from whole genome sequencing projects; provides bootstrap support to assess uncertainty in the tree topology (k-mer based)

Software (Python)

https://github.com/fanhuan/AAF

kSNP v3

Reference-free SNP identification and estimation of phylogenetic trees using SNPs (based on k-mer analysis)

Software (C)

https://sourceforge.net/projects/ksnp/files/

NGS-MC

Phylogeny of species based on NGS reads using alignment-free sequence dissimilarity measures d2* and d2 S under different Markov chain models (using k-words)

R package

http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html

Species identification/taxonomic profiling

CLARK

Taxonomic classification of metagenomic reads to known bacterial genomes using k-mer search and LCA assignment

Software (C++)

http://clark.cs.ucr.edu/

FOCUS

Reports organisms present in metagenomic samples and profiles their abundances (uses composition-based approach and non-negative least squares for prediction)

Web service Software (Python)

http://edwards.sdsu.edu/FOCUS/

GSM

Estimation of abundances of microbial genomes in metagenomic samples (k-mer based)

Software (Go)

https://github.com/pdtrang/GSM

Mash

Species identification using assembled or unassembled Illumina, PacBio, and ONT data (based on MinHash dimensionality-reduction technique)

Software (C++)

https://github.com/marbl/mash

Kraken

Taxonomic assignment in metagenome analysis by exact k-mer search; LCA assignment of short reads based on a comprehensive sequence database

Software (C++)

https://ccb.jhu.edu/software/kraken/

LMAT

Assignment of taxonomic labels to reads by k-mers searches in precomputed database

Software (C++/Python)

https://sourceforge.net/projects/lmat/

stringMLST

k-mer-based tool for MLST directly from the genome sequencing reads

Software (Python)

http://jordan.biology.gatech.edu/page/software/stringMLST

Taxonomer

k-mer-based ultrafast metagenomics tool for assigning taxonomy to sequencing reads from clinical and environmental samples

Web service

http://taxonomer.iobio.io/

Other

d2-tools

Word-based (k-tuple) comparison (pairwise dissimilarity matrix using d2S measure) of metatranscriptomic samples from NGS reads

Software (Python/R)

https://code.google.com/p/d2-tools/

VirHostMatcher

Prediction of hosts from metagenomic viral sequences based on ONF using various distance measures (e.g., d2)

Software (C++)

https://github.com/jessieren/VirHostMatcher

MetaFast

Statistics calculation of metagenome sequences and the distances between them based on assembly using de Bruijn graphs and Bray–Curtis dissimilarity measure

Software (Java)

https://github.com/ctlab/metafast

]]> Abhimanyu Singh https://bioinformaticsonline.com/pages/view/35386/list-of-visualization-tools-for-network-biology Mon, 29 Jan 2018 05:12:24 -0600 https://bioinformaticsonline.com/pages/view/35386/list-of-visualization-tools-for-network-biology <![CDATA[List of visualization tools for network biology]]> Network analysis is any structured technique used to mathematically analyze a circuit (a “network” of interconnected components). The Network analysis provides the ability to quantify associations between individuals, which makes it possible to infer details about the network as a whole at the species and/or population level. Few tools published in BMC are listed here https://bmcbioinformatics.biomedcentral.com/articles/sections/networks-analysis.

image

Following are the list of standalone applications for network analysis:

Arena 3D

3D visualization of multi-layer networks

http://www.arena3d.org

Biana

Data integration and network management

http://sbi.imim.es/web/BIANA.php

BioLayout Express 3D 

2D/3D network visualization

http://www.biolayout.org/

BiologicalNetworks 

Efficient integrated multi-level analysis of microarray, sequence, regulatory and other data

http://www.biologicalnetworks.org

BioMiner

Modeling, analyzing and visualizing biochemical pathways and networks

http://www.zbi.uni-saarland.de/chair/projects/BioMiner

Cell Illustrator 

Petri nets for modeling and simulating biological networks

http://www.cellillustrator.com

COPASI

Analysis of biochemical networks and their dynamics

http://www.copasi.org/

Cytoscape 

Network visualization and analysis. Over 200 plugins [60]

http://www.cytoscape.org/

Dizzy

Chemical kinetics stochastic simulation software

http://magnet.systemsbiology.net/software/Dizzy/

DyCoNet

Gephi plugin that can be used to identify dynamic communities in networks

https://github.com/juliemkauffman/DyCoNet

GENeVis 

Network and pathway visualization

http://tinyurl.com/genevis/

GEPHI 

Interactive visualization and exploration for any network and complex system, dynamic and hierarchical graph.

https://gephi.org

Igraph

Collection of network analysis tools with the emphasis on efficiency, portability and ease of use

http://igraph.sourceforge.net

Medusa

Semantic and multi-edged simple networks

https://sites.google.com/site/medusa3visualization/

NAViGaTOR

Visualizing and analyzing protein-protein interaction networks

http://tinyurl.com/navigator1/

N-Browse

Interactive graphical browser for biological networks

http://www.gnetbrowse.org/

NeAT

Topological and clustering analysis of networks

http://rsat.ulb.ac.be/neat/

Ondex 

Data integration and visualization of large networks

http://www.ondex.org/

Osprey

Visualization and annotation of biological networks

http://biodata.mshri.on.ca/osprey/servlet/Index

Pajek 

Analysis and visualization of large networks and social network analysis

http://vlado.fmf.uni-lj.si/pub/networks/pajek/

PathwayAssist 

Navigation and analysis of biological pathways, gene regulation networks and protein interaction maps.

http://www.ariadnegenomics.com/downloads/

PIVOT 

Layout algorithms for visualizing protein interactions and families

http://acgt.cs.tau.ac.il/pivot/

ProCope 

Prediction and evaluation of protein complexes from purification data experiments

http://www.bio.ifi.lmu.de/Complexes/ProCope/

ProViz 

Visualization and exploration of interaction networks. Gene Ontology and PSI-MI formats supported

http://cbi.labri.fr/eng/proviz.htm

SpectralNET 

Network analysis and visualizations. Scatter plots and dimensionality reduction algorithms

https://www.broadinstitute.org/software/spectralnet

Tulip 

Enables the development of algorithms, visual encodings, interaction techniques, data models and domain-specific visualizations

http://tulip.labri.fr/TulipDrupal/

VANESA 

Automatic reconstruction and analysis of biological networks and Petri nets based on life-science database information

http://agbi.techfak.uni-bielefeld.de/vanesa/

VANTED 

Network reconstruction, data visualization, integration of various data types, network simulation

http://tinyurl.com/vanted/

yEd

Creation of diagrams manually and import external data

http://tinyurl.com/yEdGraph/

Web tools for network analysis

APID 

Unified protein-protein interactions from BIND, BioGRID, DIP, HPRD, IntAct and MINT

http://bioinfow.dep.usal.es/apid/

Arcadia 

Translates text-based descriptions of biological networks (SBML files) into standardized diagrams (Systems Biology Graphical Notation Process Description maps)

http://arcadiapathways.sourceforge.net/

AVIS 

Viewer for signaling networks

http://actin.pharm.mssm.edu/AVIS2

bioPIXIE 

Discovery of biological networks from diverse functional genomic data

http://pixie.princeton.edu/pixie

CellPublisher

Interactive representations of biochemical processes

http://cellpublisher.gobics.de/

Graphle

Distributed network exploration and visualization of interactive large, dense graphs

http://tinyurl.com/graphle/

GraphWeb 

Web server for graph-based analysis of biological networks

http://biit.cs.ut.ee/graphweb/

Hubba

Web-based service to explore the essential nodes in a network

http://hub.iis.sinica.edu.tw/Hubba

NetworkBLAST 

Analysis of protein interaction networks across species to infer protein complexes that are conserved in evolution

http://www.cs.tau.ac.il/~bnet/networkblast.htm

Pathview 

Tool set for pathway-based data integration and visualization

http://Pathview.r-forge.r-project.org/

PINA 

Integrated platform for protein interaction network construction, filtering, analysis, visualization and management

http://cbg.garvan.unsw.edu.au/pina/home.do

ReMatch 

Web-based tool for integration of user-given stoichiometric metabolic models into a database collected from public data sources

http://www.cs.helsinki.fi/group/sysfys/software/rematch/

SNOW 

Gene mapping on a reference or human protein-protein interaction network that SNOW hosts

http://snow.bioinfo.cipf.es

STITCH 

Resource to explore known and predicted interactions of chemicals and proteins

http://stitch.embl.de/

STRING

Protein interaction networks and integration of data such as genomic context, high-throughput experiments, conserved coexpression and previous knowledge derived from the literature

http://string-db.org

TVNViewer 

An interactive visualization tool for exploring networks that change over time or space

http://www.sailing.cs.cmu.edu/main/?page_id=545

tYNA 

System for managing, comparing and mining multiple networks

http://tyna.gersteinlab.org/tyna/

VisANT 

Visualization, mining, analysis and modeling of biological networks, metabolic networks and ecosystems

http://visant.bu.edu/

 

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