<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/44213?offset=120 https://bioinformaticsonline.com/bookmarks/view/30336/finding-patterns-in-biological-sequences Thu, 22 Dec 2016 10:30:49 -0600 https://bioinformaticsonline.com/bookmarks/view/30336/finding-patterns-in-biological-sequences <![CDATA[Finding Patterns in Biological Sequences]]> In this report we provide an overview of known techniques for discovery of patterns of biological sequences (DNA and proteins). We also provide biological motivation, and methods of biological verification of such patterns. Finally we list publicly available tools and databases for pattern discovery. On-line supplement is available through http://genetics.uwaterloo.ca/∼tvinar/cs798g/motif.

Address of the bookmark: http://engr.case.edu/li_jing/papers/00798gpattern.pdf

]]> Jit https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads Wed, 15 Mar 2017 14:31:09 -0500 https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads <![CDATA[Software and Tools to detect structure variation with long reads !!]]> Uncovering the connection between genetics and heritable diseases requires an approach that looks at all the variant bases and types in a genome. While a PacBio de novo assembly resolves the most novel SV variants. 8-10X PacBio coverage of single genomes or trios reveals triple the SVs detectable by short-read data.

With Single Molecule, Real-Time (SMRT) Sequencing, you can access structural variations having a broad range of sizes, types, and GC content with the ability to:

  • Uncover missing heritability linked to structural variation
  • Unambiguously identify genomic context and variant breakpoints at the sequence level to unravel the genetic etiology of disease
  • Resolve structural variation across the complete size spectrum with basepair resolution

Following are the SV tools, which can assist you to achieve your goal.

Sniffles: Structural variation caller using third generation sequencing

Sniffles is a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore). It detects all types of SVs using evidence from split-read alignments, high-mismatch regions, and coverage analysis. Please note the current version of Sniffles requires sorted output from BWA-MEM (use -M and -x parameter) or NGM-LR with the optional SAM attributes enabled! 

More at https://github.com/fritzsedlazeck/Sniffles


MultiBreak-SV: It identifies structural variants from next-generation paired end data, third-generation long read data, or data from a combination of sequencing platforms.

There are two pieces of software in this release: (1) a pre-processor that takes machineformat (.m5) BLASR files, and (2) MultiBreak-SV. For installation and usage instructions, see doc/MultiBreakSV-Manual.txt.

More at https://github.com/raphael-group/multibreak-sv


Parliament: A Structural Variation Tool. Why ask a single sv-detection approach to find every variant when you can have a parliament of tools deciding?

Publication about the algorithm and “…the first long-read characterization of structural variation in a diploid human personal genome…” (HS1011) - “Assessing structural variation in a personal genome—towards a human reference diploid genome”

More at https://sourceforge.net/projects/parliamentsv/

https://www.dnanexus.com/papers/Parliament_Info_Sheet.pdf


PBHoney: the structural variation discovery tool 

PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.

Read The Paper http://www.biomedcentral.com/1471-2105/15/180/abstract

More at https://sourceforge.net/projects/pb-jelly/


SMRT-SV: Structural variant and indel caller for PacBio reads

Structural variant (SV) and indel caller for PacBio reads based on methods from Chaisson et al. 2014.

SMRT-SV provides an official software package for tools described in Chaisson et al. 2014 and adds several key features including the following.

  • Unified variant calling user interface with built-in cluster compute support
  • Small indel calling (2-49 bp)
  • Improved inversion calling (screenInversions)
  • Quality metric for SV calls based on number of local assemblies supporting each call
  • Higher sensitivity for SV calls using tiled local assemblies across the entire genome instead of "signature" regions
  • Genotyping of SVs with Illumina paired-end reads from WGS samples

More at https://github.com/EichlerLab/pacbio_variant_caller

]]> Archana Malhotra 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/36395/ligand-docking-tools-and-software Wed, 25 Apr 2018 05:05:17 -0500 https://bioinformaticsonline.com/pages/view/36395/ligand-docking-tools-and-software <![CDATA[Ligand Docking Tools and Software !]]> Ligand docking referred to cases where small molecule (“ligand”) is being docked into much larger macromolecule ("target"). The following is partial list of docking software, focusing on free (at least for academic institutes) and/or popular docking tools. 

AutoDock

Stochastic (GA)

Flexible ligand and partially flexible target

ArgusLab

Systematic

Flexible ligandX-Score based

DOCK

Systematic (IC)

Flexible ligandDOCK 3.5 (force field)

eHITS

Systematic (RBD of fragments followed by reconstruction)Flexible ligand and partially flexible targetHiTS_Score (empirical)

FlexX

Systematic (IC)Flexible ligandFlexX SF (empirical)Commercial

FLIPDock

Stochastic (GA)Flexible ligand and flexible targetAUTODOCK (empirical)

FRED

Systematic (RBD)Flexible ligandChemScore, PLP, ScreenScore, ChemGauss (empirical/consensus)

GOLD

Stochastic (GA)

Flexible ligand and partially flexible targetGoldScore, ChemScore (empirical), ASP (knowledge based)

ICM

Stochastic (MC)

Flexible ligand and partially flexible targetICM SF (empirical)

ParDOCK

Stochastic (MC)

RigidBAPPL (empirical)

PLANTS

Stochastic (ACO)Flexible ligand and partially flexible target

CHEMPLP, PLP (empirical)

Surflex

Systematic (IC/MA)Flexible ligandHammerhead based (empirical)

Point to note:

Several studies have shown that the performance of most docking tools is highly dependent on the particular characteristics of both the binding site and the ligand to be investigated, and the determination which method would be more suitable in a specific context is difficult. We encouraged you to check several docking methods to determine which one(s) work best for your system.

 

]]> Poonam Mahapatra https://bioinformaticsonline.com/bookmarks/view/36510/scallop-reference-based-transcriptome-assembler-for-rna-seq Tue, 08 May 2018 04:23:27 -0500 https://bioinformaticsonline.com/bookmarks/view/36510/scallop-reference-based-transcriptome-assembler-for-rna-seq <![CDATA[Scallop: reference-based transcriptome assembler for RNA-seq]]> Scallop is an accurate reference-based transcript assembler. Scallop features its high accuracy in assembling multi-exon transcripts as well as lowly expressed transcripts. Scallop achieves this improvement through a novel algorithm that can be proved preserving all phasing paths from reads and paired-end reads, while also achieves both transcripts parsimony and coverage deviation minimization.

Scallop paper has been published at Nature Biotechnology. The datasets and scripts used in this paper to compare the performance of Scallop and other assemblers are available at scalloptest.

Please also checkout the podcast about Scallop (thanks Roman Cheplyaka for the interview). It is available at both the bioinformatics chat and iTunes.

 

https://github.com/Kingsford-Group/scallop

Address of the bookmark: https://github.com/Kingsford-Group/scallop

]]> 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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Err:11 http://packages.cloud.google.com/apt cloud-sdk-xenial InRelease 
The following signatures couldn't be verified because the public key is not available: NO_PUBKEY 6A030B21BA07F4FB
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Fetched 4.779 kB in 2s (1.606 kB/s) 
Reading package lists... Done
W: An error occurred during the signature verification. The repository is not updated and the previous index files will be used. GPG error: http://packages.cloud.google.com/apt cloud-sdk-xenial InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY 6A030B21BA07F4FB
W: Failed to fetch http://packages.cloud.google.com/apt/dists/cloud-sdk-xenial/InRelease The following signatures couldn't be verified because the public key is not available: NO_PUBKEY 6A030B21BA07F4FB
W: Some index files failed to download. They have been ignored, or old ones used instead.
➜ 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
0 upgraded, 64 newly installed, 0 to remove and 11 not upgraded.
Need to get 15,2 MB of archives.
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Get:53 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5concurrent5 amd64 5.5.1+dfsg-16ubuntu7.5 [24,3 kB]
Get:54 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5opengl5 amd64 5.5.1+dfsg-16ubuntu7.5 [128 kB]
Get:55 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5printsupport5 amd64 5.5.1+dfsg-16ubuntu7.5 [174 kB]
Get:56 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5sql5 amd64 5.5.1+dfsg-16ubuntu7.5 [104 kB]
Get:57 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5test5 amd64 5.5.1+dfsg-16ubuntu7.5 [84,5 kB]
Get:58 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5xml5 amd64 5.5.1+dfsg-16ubuntu7.5 [91,8 kB]
Get:59 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 qt5-qmake amd64 5.5.1+dfsg-16ubuntu7.5 [1.192 kB]
Get:60 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 qtbase5-dev-tools amd64 5.5.1+dfsg-16ubuntu7.5 [1.056 kB]
Get:61 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 qtbase5-dev amd64 5.5.1+dfsg-16ubuntu7.5 [931 kB]
Get:62 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5opengl5-dev amd64 5.5.1+dfsg-16ubuntu7.5 [31,5 kB]
Get:63 http://be.archive.ubuntu.com/ubuntu xenial-updates/main amd64 libqt5sql5-sqlite amd64 5.5.1+dfsg-16ubuntu7.5 [33,0 kB]
Get:64 http://be.archive.ubuntu.com/ubuntu xenial/main amd64 qttranslations5-l10n all 5.5.1-2build1 [1.210 kB]
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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/bookmarks/view/43546/introduction-to-phylogenies-in-r Wed, 13 Oct 2021 02:27:21 -0500 https://bioinformaticsonline.com/bookmarks/view/43546/introduction-to-phylogenies-in-r <![CDATA[Introduction to phylogenies in R]]> R phylogenetics is built on the contributed packages for phylogenetics in R, and there are many such packages. Let's begin today by installing a few critical packages, such as ape, phangorn, phytools, and geiger. To get the most recent CRAN version of these packages, you will need to have R 3.3.x installed on your computer!

Address of the bookmark: http://www.phytools.org/Cordoba2017/ex/2/Intro-to-phylogenies.html

]]> Abhi 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/blog/view/44731/exploring-bacterial-comparative-genomics-a-bioinformatics-approach Sat, 14 Dec 2024 12:31:14 -0600 https://bioinformaticsonline.com/blog/view/44731/exploring-bacterial-comparative-genomics-a-bioinformatics-approach <![CDATA[Exploring Bacterial Comparative Genomics: A Bioinformatics Approach]]> In the world of microbiology, bacteria have long fascinated scientists for their diversity, adaptability, and crucial roles in ecosystems and human health. Comparative genomics—a field that involves analyzing and comparing the genomes of different organisms—has revolutionized our understanding of bacterial evolution, adaptation, and pathogenicity. By leveraging bioinformatics tools and techniques, researchers can uncover genomic insights that were once hidden. This blog delves into the principles, methodologies, and applications of bacterial comparative genomics from a bioinformatics perspective.

What is Bacterial Comparative Genomics?

Comparative genomics involves the systematic comparison of genomes across different bacterial species or strains. This approach allows scientists to:

  • Identify conserved and unique genes.

  • Explore genetic determinants of pathogenicity.

  • Understand bacterial evolution and phylogenetics.

  • Investigate horizontal gene transfer and its role in antibiotic resistance.

Bioinformatics is central to these analyses, enabling the processing and interpretation of large-scale genomic data.

Key Steps in Bacterial Comparative Genomics

  1. Genome Sequencing and Assembly: The process begins with obtaining high-quality bacterial genome sequences. Advances in next-generation sequencing (NGS) technologies have made it faster and more affordable to sequence bacterial genomes. Tools such as SPAdes and Velvet are commonly used for genome assembly.

  2. Genome Annotation: Annotating a genome involves identifying genes, regulatory elements, and other genomic features. Automated tools like Prokka and RAST provide functional annotations, allowing researchers to predict the roles of genes and proteins.

  3. Genome Alignment: Aligning genomes is crucial for identifying conserved regions, single-nucleotide polymorphisms (SNPs), and structural variations. Tools like Mauve and progressiveMauve are commonly employed for whole-genome alignments.

  4. Comparative Analyses:

    • Core and Pan-genome Analysis: The core genome consists of genes shared across all strains of a species, while the pan-genome includes all genes found in any strain. Software like Roary and BPGA can perform core and pan-genome analyses.

    • Phylogenetic Analysis: Comparative genomics often involves reconstructing evolutionary relationships. Tools such as MEGA and IQ-TREE facilitate phylogenetic tree construction based on genomic data.

    • Functional Enrichment Analysis: To understand the biological significance of unique or shared genes, functional enrichment analysis using databases like GO (Gene Ontology) and KEGG is essential.

 Recommended Bioinformatics Tools for Comparative Genomics

Here are some additional bioinformatics tools that can aid bacterial comparative genomics:

  • OrthoFinder: For accurate ortholog identification across multiple genomes.

  • PanOCT: Specifically designed for pan-genome clustering and annotation.

  • FASTANI: A tool for calculating Average Nucleotide Identity (ANI) for microbial genome comparisons.

  • CIRCOS: For visually comparing genomic data through circular genome plots.

  • Galaxy Platform: A user-friendly web-based platform offering numerous genomic analysis tools.

  • BLAST: Essential for sequence alignment and similarity searches.

  • PhyloSift: Focused on phylogenetic analysis of microbial genomes using marker genes.

These tools, in combination with the methods discussed, provide a robust framework for conducting comprehensive comparative genomic studies.

Applications of Bacterial Comparative Genomics

  1. Understanding Pathogenicity: Comparative genomics helps identify virulence factors that distinguish pathogenic strains from non-pathogenic relatives. For instance, comparing genomes of Escherichia coli strains has revealed key genetic determinants of pathogenicity in enterohemorrhagic strains.

  2. Antibiotic Resistance Research: The spread of antibiotic resistance genes through horizontal gene transfer is a major global concern. Comparative analyses can trace the origins and dissemination of resistance genes, aiding in the development of countermeasures.

  3. Microbial Ecology and Evolution: By studying genomic variations, researchers can understand how bacteria adapt to different environments. This is particularly relevant for extremophiles and symbiotic bacteria.

  4. Vaccine Development: Identifying conserved antigens across pathogenic strains is critical for vaccine design. Comparative genomics has been instrumental in developing vaccines against pathogens like Neisseria meningitidis.

  5. Biotechnology Applications: Comparative studies can uncover unique metabolic pathways in bacteria, paving the way for applications in bioremediation, synthetic biology, and industrial microbiology.

Challenges in Bacterial Comparative Genomics

While the field has made significant strides, several challenges remain:

  • Data Overload: The rapid growth of sequencing data requires robust computational infrastructure and efficient algorithms.

  • Genome Plasticity: High rates of horizontal gene transfer and genome rearrangements in bacteria complicate comparative analyses.

  • Annotation Accuracy: Automated annotation tools are not infallible, and manual curation is often needed for high-confidence results.

  • Interpreting Non-Coding Regions: Understanding the functional significance of non-coding genomic regions remains a challenge.

Future Directions

The integration of bacterial comparative genomics with other ‘omics’ approaches—such as transcriptomics, proteomics, and metabolomics—promises a more comprehensive understanding of bacterial biology. Additionally, advancements in machine learning and artificial intelligence are likely to further enhance bioinformatics analyses, enabling the prediction of complex phenotypes from genomic data.

Conclusion

Bacterial comparative genomics, driven by bioinformatics, continues to unravel the complexities of bacterial life. From combating antibiotic resistance to uncovering the secrets of microbial evolution, this interdisciplinary field holds immense potential for addressing pressing challenges in microbiology and beyond. As technology advances, so too will our ability to harness the power of comparative genomics for scientific and societal benefit.

]]> LEGE https://bioinformaticsonline.com/fun/view/4196/chemical-elements-of-bioinformatics Tue, 03 Sep 2013 16:35:39 -0500 https://bioinformaticsonline.com/fun/view/4196/chemical-elements-of-bioinformatics <![CDATA[Chemical Elements of Bioinformatics]]> You must be familiar with periodic table and colour pattern, but this time you are going to amaze by new elements table by Eagle genomics. Just check it out and have fun :)

http://elements.eaglegenomics.com/

]]> Rahul Agarwal