<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/44770?offset=540 https://bioinformaticsonline.com/bookmarks/view/34485/phyloxml-xml-for-evolutionary-biology-and-comparative-genomics Wed, 29 Nov 2017 10:04:48 -0600 https://bioinformaticsonline.com/bookmarks/view/34485/phyloxml-xml-for-evolutionary-biology-and-comparative-genomics <![CDATA[phyloXML: XML for evolutionary biology and comparative genomics]]> phyloXML (example) is an XML language designed to describe phylogenetic trees (or networks) and associated data. PhyloXML provides elements for commonly used features, such as taxonomic information, gene names and identifiers, branch lengths, support values, and gene duplication and speciation events. Using these standardized elements allows interoperability between various applications and databases. Furthermore, both due to extensible nature of XML itself and the provision of <property> elements by phyloXML, extensibility as well as domain specific applications are ensured. The structure of phyloXML is described by XML Schema Definition (XSD) language.

Address of the bookmark: http://www.phyloxml.org/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/36267/pspairwise-sequentially-markovian-coalescent-psmc-model Thu, 19 Apr 2018 05:29:23 -0500 https://bioinformaticsonline.com/bookmarks/view/36267/pspairwise-sequentially-markovian-coalescent-psmc-model <![CDATA[PSPairwise Sequentially Markovian Coalescent (PSMC) model]]> Implementation of the Pairwise Sequentially Markovian Coalescent (PSMC) model

Address of the bookmark: https://github.com/lh3/psmc

]]> Jit https://bioinformaticsonline.com/opportunity/view/40305/naab-doak-graduate-fellowship-bovine-genomics-course Thu, 28 Nov 2019 21:45:35 -0600 <![CDATA[NAAB Doak Graduate Fellowship bovine genomics course]]> This is a reminder for all that seek a fully funded MSc in bovine genetics, or those that know talented BSc students who want to progress their education.

The deadline for the NAAB Doak Graduate Fellowship is less than a month away.

Applications are accepted until the 1st of December.

Please check the attachment or visit our website for further details:

https://www.naab-css.org/news/-naab-doak-graduate-fellowship

Sophie Eaglen
NAAB

]]> https://bioinformaticsonline.com/bookmarks/view/41009/genomics-public-data-links Thu, 13 Feb 2020 00:20:00 -0600 https://bioinformaticsonline.com/bookmarks/view/41009/genomics-public-data-links <![CDATA[genomics public data links !]]> List of publically available databases on google server.

More at https://software.broadinstitute.org/gatk/download/bundle

ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606/VCF/GATK/.

ftp://ftp.broadinstitute.org/bundle/hg38/hg38bundle/

Address of the bookmark: https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0?pli=1

]]> Jit https://bioinformaticsonline.com/bookmarks/view/42713/gggenomes-a-grammar-of-graphics-for-comparative-genomics Mon, 01 Feb 2021 14:47:32 -0600 https://bioinformaticsonline.com/bookmarks/view/42713/gggenomes-a-grammar-of-graphics-for-comparative-genomics <![CDATA[gggenomes: A grammar of graphics for comparative genomics]]> gggenomes is a versatile graphics package for comparative genomics. It extends the popular R visualization packageggplot2 by adding dedicated plot functions for genes, syntenic regions, etc. and verbs to manipulate the plot to, for example, quickly zoom in into gene neighborhoods.

Address of the bookmark: https://github.com/thackl/gggenomes

]]> Surabhi Chaudhary https://bioinformaticsonline.com/opportunity/view/43928/bioinformaticians-in-comparative-and-evolutionary-genomics Tue, 02 Aug 2022 01:22:48 -0500 <![CDATA[Bioinformaticians in comparative and evolutionary genomics]]> NBIS is now looking for a new member to support Swedish research in evolutionary, comparative, and population genomics, with a particular focus on conifer genomics.

Your tasks will consist of:

Advanced bioinformatics analyses within research projects across Sweden, including key involvement in a major research effort in conifer genomics.
Development of bioinformatics tools and workflows.
Educating other scientists in bioinformatics through collaboration within supported projects, teaching at national courses, and through participating in various networks.
Taking part in the continuous development of NBIS/SciLifeLab at a national level

More at https://www.uu.se/en/about-uu/join-us/details/?positionId=518909

]]> https://bioinformaticsonline.com/blog/view/45177/installing-crossroad-on-ubuntu Fri, 29 May 2026 05:19:45 -0500 https://bioinformaticsonline.com/blog/view/45177/installing-crossroad-on-ubuntu <![CDATA[Installing croSSRoad on Ubuntu !]]> (base) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$ conda
usage: conda [-h] [-v] [--no-plugins] [-V] COMMAND ...

conda is a tool for managing and deploying applications, environments and packages.

options:
-h, --help Show this help message and exit.
-v, --verbose Can be used multiple times. Once for detailed output, twice for INFO logging, thrice for DEBUG logging, four times for TRACE logging.
--no-plugins Disable all plugins that are not built into conda.
-V, --version Show the conda version number and exit.

commands:
The following built-in and plugins subcommands are available.

COMMAND
activate Activate a conda environment.
clean Remove unused packages and caches.
commands List all available conda subcommands (including those from plugins). Generally only used by tab-completion.
compare Compare packages between conda environments.
config Modify configuration values in .condarc.
create Create a new conda environment from a list of specified packages.
deactivate Deactivate the current active conda environment.
doctor Display a health report for your environment.
env Create and manage conda environments.
export Export a given environment
info Display information about current conda install.
init Initialize conda for shell interaction.
install Install a list of packages into a specified conda environment.
list List installed packages in a conda environment.
notices Retrieve latest channel notifications.
package Create low-level conda packages. (EXPERIMENTAL)
remove (uninstall) Remove a list of packages from a specified conda environment.
rename Rename an existing environment.
repoquery Advanced search for repodata.
run Run an executable in a conda environment.
search Search for packages and display associated information using the MatchSpec format.
update (upgrade) Update conda packages to the latest compatible version.
(base) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$ conda create -n jitENV
Retrieving notices: done
Channels:
- ursky
- bioconda
- conda-forge
Platform: linux-64
Collecting package metadata (repodata.json): done
Solving environment: done


==> WARNING: A newer version of conda exists. <==
current version: 25.7.0
latest version: 26.5.0

Please update conda by running

$ conda update -n base -c conda-forge conda

 

## Package Plan ##

environment location: /home/hp/miniforge3/envs/jitENV

 

Proceed ([y]/n)? y


Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate jitENV
#
# To deactivate an active environment, use
#
# $ conda deactivate

(base) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$ conda activate jitENV
(jitENV) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$ conda install conda-forge::mamba
Channels:
- ursky
- bioconda
- conda-forge
Platform: linux-64
Collecting package metadata (repodata.json): done
Solving environment: done


==> WARNING: A newer version of conda exists. <==
current version: 25.7.0
latest version: 26.5.0

Please update conda by running

$ conda update -n base -c conda-forge conda

 

## Package Plan ##

environment location: /home/hp/miniforge3/envs/jitENV

added / updated specs:
- conda-forge::mamba


The following packages will be downloaded:

package | build
---------------------------|-----------------
ca-certificates-2026.5.20 | hbd8a1cb_0 127 KB conda-forge
cpp-expected-1.3.1 | h171cf75_0 24 KB conda-forge
fmt-12.1.0 | hff5e90c_0 193 KB conda-forge
libarchive-3.8.7 | gpl_hc2c16d8_101 869 KB conda-forge
libcurl-8.20.0 | hcf29cc6_0 458 KB conda-forge
libgcc-15.2.0 | he0feb66_19 1017 KB conda-forge
libgcc-ng-15.2.0 | h69a702a_19 27 KB conda-forge
libgomp-15.2.0 | he0feb66_19 590 KB conda-forge
libmamba-2.6.2 | hd28c85e_0 2.7 MB conda-forge
libmsgpack-c-6.1.0 | h54a6638_6 39 KB conda-forge
libsolv-0.7.38 | h9463b59_0 509 KB conda-forge
libstdcxx-15.2.0 | h934c35e_19 5.6 MB conda-forge
libxml2-2.15.3 | h49c6c72_0 46 KB conda-forge
libxml2-16-2.15.3 | hca6bf5a_0 547 KB conda-forge
mamba-2.6.2 | hce6dcdd_0 553 KB conda-forge
ncurses-6.6 | hdb14827_0 897 KB conda-forge
nlohmann_json-abi-3.12.0 | h0f90c79_1 4 KB conda-forge
reproc-14.2.7.post0 | hb03c661_1 35 KB conda-forge
reproc-cpp-14.2.7.post0 | hecca717_1 26 KB conda-forge
simdjson-4.6.4 | hb700be7_0 310 KB conda-forge
spdlog-1.17.0 | hab81395_1 192 KB conda-forge
------------------------------------------------------------
Total: 14.6 MB

The following NEW packages will be INSTALLED:

_openmp_mutex conda-forge/linux-64::_openmp_mutex-4.5-20_gnu
bzip2 conda-forge/linux-64::bzip2-1.0.8-hda65f42_9
c-ares conda-forge/linux-64::c-ares-1.34.6-hb03c661_0
ca-certificates conda-forge/noarch::ca-certificates-2026.5.20-hbd8a1cb_0
cpp-expected conda-forge/linux-64::cpp-expected-1.3.1-h171cf75_0
fmt conda-forge/linux-64::fmt-12.1.0-hff5e90c_0
icu conda-forge/linux-64::icu-78.3-h33c6efd_0
keyutils conda-forge/linux-64::keyutils-1.6.3-hb9d3cd8_0
krb5 conda-forge/linux-64::krb5-1.22.2-ha1258a1_0
libarchive conda-forge/linux-64::libarchive-3.8.7-gpl_hc2c16d8_101
libcurl conda-forge/linux-64::libcurl-8.20.0-hcf29cc6_0
libedit conda-forge/linux-64::libedit-3.1.20250104-pl5321h7949ede_0
libev conda-forge/linux-64::libev-4.33-hd590300_2
libgcc conda-forge/linux-64::libgcc-15.2.0-he0feb66_19
libgcc-ng conda-forge/linux-64::libgcc-ng-15.2.0-h69a702a_19
libgomp conda-forge/linux-64::libgomp-15.2.0-he0feb66_19
libiconv conda-forge/linux-64::libiconv-1.18-h3b78370_2
liblzma conda-forge/linux-64::liblzma-5.8.3-hb03c661_0
libmamba conda-forge/linux-64::libmamba-2.6.2-hd28c85e_0
libmsgpack-c conda-forge/linux-64::libmsgpack-c-6.1.0-h54a6638_6
libnghttp2 conda-forge/linux-64::libnghttp2-1.68.1-h877daf1_0
libsolv conda-forge/linux-64::libsolv-0.7.38-h9463b59_0
libssh2 conda-forge/linux-64::libssh2-1.11.1-hcf80075_0
libstdcxx conda-forge/linux-64::libstdcxx-15.2.0-h934c35e_19
libxml2 conda-forge/linux-64::libxml2-2.15.3-h49c6c72_0
libxml2-16 conda-forge/linux-64::libxml2-16-2.15.3-hca6bf5a_0
libzlib conda-forge/linux-64::libzlib-1.3.2-h25fd6f3_2
lz4-c conda-forge/linux-64::lz4-c-1.10.0-h5888daf_1
lzo conda-forge/linux-64::lzo-2.10-h280c20c_1002
mamba conda-forge/linux-64::mamba-2.6.2-hce6dcdd_0
ncurses conda-forge/linux-64::ncurses-6.6-hdb14827_0
nlohmann_json-abi conda-forge/noarch::nlohmann_json-abi-3.12.0-h0f90c79_1
openssl conda-forge/linux-64::openssl-3.6.2-h35e630c_0
reproc conda-forge/linux-64::reproc-14.2.7.post0-hb03c661_1
reproc-cpp conda-forge/linux-64::reproc-cpp-14.2.7.post0-hecca717_1
simdjson conda-forge/linux-64::simdjson-4.6.4-hb700be7_0
spdlog conda-forge/linux-64::spdlog-1.17.0-hab81395_1
yaml-cpp conda-forge/linux-64::yaml-cpp-0.8.0-h3f2d84a_0
zstd conda-forge/linux-64::zstd-1.5.7-hb78ec9c_6


Proceed ([y]/n)? y


Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(jitENV) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$ mamba install -c jitendralab -c bioconda -c conda-forge crossroad -y
jitendralab/noarch ??.?MB @ ??.?MB/s 0.3s
jitendralab/linux-64 ??.?MB @ ??.?MB/s 0.4s
bioconda/linux-64 5.6MB @ 2.9MB/s 1.9s
bioconda/noarch 5.6MB @ 2.5MB/s 2.2s
conda-forge/noarch 26.4MB @ 6.0MB/s 4.5s
conda-forge/linux-64 53.8MB @ 6.7MB/s 8.2s


Transaction

Prefix: /home/hp/miniforge3/envs/jitENV

Updating specs:

- crossroad

Package Version Build Channel Size
─────────────────────────────────────────────────────────────────────────────────────────────────
Install:
─────────────────────────────────────────────────────────────────────────────────────────────────

+ annotated-doc 0.0.4 pyhcf101f3_0 conda-forge Cached
+ annotated-types 0.7.0 pyhd8ed1ab_1 conda-forge Cached
+ anyio 4.13.0 pyhcf101f3_0 conda-forge 147kB
+ argcomplete 3.6.3 pyhd8ed1ab_0 conda-forge Cached
+ aws-c-auth 0.10.3 h3aafcba_1 conda-forge 134kB
+ aws-c-cal 0.9.14 h8e43964_1 conda-forge 57kB
+ aws-c-common 0.13.1 hb03c661_0 conda-forge 242kB
+ aws-c-compression 0.3.2 h16e98cb_1 conda-forge 22kB
+ aws-c-event-stream 0.7.1 h9be7a74_1 conda-forge 59kB
+ aws-c-http 0.11.0 hcbcd92d_1 conda-forge 230kB
+ aws-c-io 0.26.3 h955231c_3 conda-forge 182kB
+ aws-c-mqtt 0.15.2 h8af55cf_3 conda-forge 222kB
+ aws-c-s3 0.12.3 h00bea6e_2 conda-forge 153kB
+ aws-c-sdkutils 0.2.4 h16e98cb_5 conda-forge 59kB
+ aws-checksums 0.2.10 h16e98cb_1 conda-forge 102kB
+ aws-crt-cpp 0.38.3 h7b0d4b4_2 conda-forge 413kB
+ aws-sdk-cpp 1.11.747 h5a171d8_5 conda-forge 4MB
+ azure-core-cpp 1.16.2 h206d751_0 conda-forge 349kB
+ azure-identity-cpp 1.13.3 hed0cdb0_1 conda-forge 251kB
+ azure-storage-blobs-cpp 12.17.0 hf824e48_1 conda-forge 587kB
+ azure-storage-common-cpp 12.13.0 ha7a2c86_0 conda-forge 159kB
+ azure-storage-files-datalake-cpp 12.15.0 h1e5b466_0 conda-forge 304kB
+ backports.zstd 1.5.0 py314h680f03e_0 conda-forge 8kB
+ bedtools 2.31.1 h13024bc_3 bioconda Cached
+ biopython 1.87 py314h5bd0f2a_0 conda-forge 3MB
+ brotli 1.2.0 hed03a55_1 conda-forge Cached
+ brotli-bin 1.2.0 hb03c661_1 conda-forge Cached
+ brotli-python 1.2.0 py314h3de4e8d_1 conda-forge 367kB
+ certifi 2026.5.20 pyhd8ed1ab_0 conda-forge 134kB
+ charset-normalizer 3.4.7 pyhd8ed1ab_0 conda-forge Cached
+ click 8.4.1 pyhc90fa1f_0 conda-forge 105kB
+ colorama 0.4.6 pyhd8ed1ab_1 conda-forge Cached
+ contourpy 1.3.3 py314h97ea11e_4 conda-forge 324kB
+ crossroad 0.3.6 pyh7e60211_0 jitendralab 2MB
+ cycler 0.12.1 pyhcf101f3_2 conda-forge Cached
+ dnspython 2.8.0 pyhcf101f3_0 conda-forge Cached
+ email-validator 2.3.0 pyhd8ed1ab_0 conda-forge 47kB
+ email_validator 2.3.0 hd8ed1ab_0 conda-forge 7kB
+ exceptiongroup 1.3.1 pyhd8ed1ab_0 conda-forge Cached
+ expat 2.8.1 hecca717_0 conda-forge 148kB
+ fastapi 0.136.3 h5ddb490_0 conda-forge 5kB
+ fastapi-cli 0.0.23 pyhcf101f3_0 conda-forge 19kB
+ fastapi-core 0.136.3 pyhcf101f3_0 conda-forge 96kB
+ fastar 0.11.0 py314h0b738fb_0 conda-forge 423kB
+ font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge Cached
+ font-ttf-inconsolata 3.000 h77eed37_0 conda-forge Cached
+ font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge Cached
+ font-ttf-ubuntu 0.83 h77eed37_3 conda-forge Cached
+ fontconfig 2.18.0 h27c8c51_0 conda-forge 281kB
+ fonts-conda-forge 1 hc364b38_1 conda-forge Cached
+ fonttools 4.63.0 pyh7db6752_0 conda-forge 846kB
+ freetype 2.14.3 ha770c72_0 conda-forge Cached
+ gflags 2.2.2 h5888daf_1005 conda-forge 120kB
+ glog 0.7.1 hbabe93e_0 conda-forge 143kB
+ h11 0.16.0 pyhcf101f3_1 conda-forge 39kB
+ h2 4.3.0 pyhcf101f3_0 conda-forge Cached
+ hpack 4.1.0 pyhd8ed1ab_0 conda-forge Cached
+ httpcore 1.0.9 pyh29332c3_0 conda-forge Cached
+ httptools 0.7.1 py314h5bd0f2a_1 conda-forge 99kB
+ httpx 0.28.1 pyhd8ed1ab_0 conda-forge Cached
+ hyperframe 6.1.0 pyhd8ed1ab_0 conda-forge Cached
+ idna 3.17 pyhcf101f3_0 conda-forge 57kB
+ jinja2 3.1.6 pyhcf101f3_1 conda-forge Cached
+ kaleido-core 0.2.1 h3644ca4_0 conda-forge Cached
+ kiwisolver 1.5.0 py314h97ea11e_0 conda-forge 77kB
+ lcms2 2.19.1 h0c24ade_0 conda-forge 251kB
+ ld_impl_linux-64 2.45.1 default_hbd61a6d_102 conda-forge Cached
+ lerc 4.1.0 hdb68285_0 conda-forge Cached
+ libabseil 20260107.1 cxx17_h7b12aa8_0 conda-forge 1MB
+ libarrow 24.0.0 h6f10b76_3_cpu conda-forge 7MB
+ libarrow-acero 24.0.0 h635bf11_3_cpu conda-forge 592kB
+ libarrow-compute 24.0.0 h53684a4_3_cpu conda-forge 3MB
+ libarrow-dataset 24.0.0 h635bf11_3_cpu conda-forge 592kB
+ libarrow-substrait 24.0.0 hb4dd7c2_3_cpu conda-forge 502kB
+ libblas 3.11.0 8_h4a7cf45_openblas conda-forge 19kB
+ libbrotlicommon 1.2.0 hb03c661_1 conda-forge Cached
+ libbrotlidec 1.2.0 hb03c661_1 conda-forge Cached
+ libbrotlienc 1.2.0 hb03c661_1 conda-forge Cached
+ libcblas 3.11.0 8_h0358290_openblas conda-forge 19kB
+ libcrc32c 1.1.2 h9c3ff4c_0 conda-forge Cached
+ libdeflate 1.25 h17f619e_0 conda-forge Cached
+ libevent 2.1.12 hf998b51_1 conda-forge Cached
+ libexpat 2.8.1 hecca717_0 conda-forge 77kB
+ libffi 3.5.2 h3435931_0 conda-forge Cached
+ libfreetype 2.14.3 ha770c72_0 conda-forge Cached
+ libfreetype6 2.14.3 h73754d4_0 conda-forge Cached
+ libgfortran 15.2.0 h69a702a_19 conda-forge 28kB
+ libgfortran5 15.2.0 h68bc16d_19 conda-forge 2MB
+ libgoogle-cloud 3.5.0 h25dbb67_0 conda-forge 3MB
+ libgoogle-cloud-storage 3.5.0 hdbdcf42_0 conda-forge 780kB
+ libgrpc 1.78.1 h1d1128b_0 conda-forge 7MB
+ libjpeg-turbo 3.1.4.1 hb03c661_0 conda-forge Cached
+ liblapack 3.11.0 8_h47877c9_openblas conda-forge 19kB
+ libmpdec 4.0.0 hb03c661_1 conda-forge 92kB
+ libopenblas 0.3.33 pthreads_h94d23a6_0 conda-forge 6MB
+ libopentelemetry-cpp 1.26.0 h9692893_0 conda-forge 934kB
+ libopentelemetry-cpp-headers 1.26.0 ha770c72_0 conda-forge 396kB
+ libparquet 24.0.0 h7376487_3_cpu conda-forge 1MB
+ libpng 1.6.58 h421ea60_0 conda-forge 318kB
+ libprotobuf 6.33.5 h6eeba95_1 conda-forge 4MB
+ libre2-11 2025.11.05 h0dc7533_1 conda-forge 213kB
+ libsqlite 3.53.1 h0c1763c_0 conda-forge 955kB
+ libstdcxx-ng 15.2.0 hdf11a46_19 conda-forge 28kB
+ libthrift 0.22.0 h7d032f7_2 conda-forge 424kB
+ libtiff 4.7.1 h9d88235_1 conda-forge Cached
+ libutf8proc 2.11.3 hfe17d71_0 conda-forge 86kB
+ libuuid 2.42.1 h5347b49_0 conda-forge 40kB
+ libuv 1.52.1 h280c20c_0 conda-forge 420kB
+ libwebp-base 1.6.0 hd42ef1d_0 conda-forge Cached
+ libxcb 1.17.0 h8a09558_0 conda-forge Cached
+ markdown-it-py 4.2.0 pyhd8ed1ab_0 conda-forge 69kB
+ markupsafe 3.0.3 py314h67df5f8_1 conda-forge 27kB
+ mathjax 2.7.7 ha770c72_3 conda-forge Cached
+ matplotlib-base 3.10.9 py314h1194b4b_0 conda-forge 9MB
+ mdurl 0.1.2 pyhd8ed1ab_1 conda-forge Cached
+ munkres 1.0.7 py_1 bioconda Cached
+ narwhals 2.21.2 pyhcf101f3_0 conda-forge 284kB
+ nlohmann_json 3.12.0 h54a6638_1 conda-forge 136kB
+ nspr 4.38 h29cc59b_0 conda-forge Cached
+ nss 3.118 h445c969_0 conda-forge Cached
+ numpy 2.4.6 py314h2b28147_0 conda-forge 9MB
+ openjpeg 2.5.4 h55fea9a_0 conda-forge Cached
+ orc 2.3.0 h21090e2_0 conda-forge 1MB
+ packaging 26.2 pyhc364b38_0 conda-forge 92kB
+ pandas 3.0.3 py314hb4ffadd_0 conda-forge 15MB
+ perf_ssr 0.4.8 py_0 jitendralab 720kB
+ pillow 12.2.0 py314h8ec4b1a_0 conda-forge 1MB
+ pip 26.1.1 pyh145f28c_0 conda-forge 1MB
+ plotly 6.6.0 pyhd8ed1ab_0 conda-forge Cached
+ plotly-upset-hd 0.0.2 py_0 jitendralab 356kB
+ prometheus-cpp 1.3.0 ha5d0236_0 conda-forge 200kB
+ pthread-stubs 0.4 hb9d3cd8_1002 conda-forge Cached
+ pyarrow 24.0.0 py314hdafbbf9_0 conda-forge 27kB
+ pyarrow-core 24.0.0 py314h969be7f_0_cpu conda-forge 5MB
+ pydantic 2.13.4 pyhcf101f3_0 conda-forge 347kB
+ pydantic-core 2.46.4 py314h2e6c369_0 conda-forge 2MB
+ pydantic-extra-types 2.11.2 pyhcf101f3_0 conda-forge 74kB
+ pydantic-settings 2.14.1 pyhcf101f3_0 conda-forge 52kB
+ pygments 2.20.0 pyhd8ed1ab_0 conda-forge Cached
+ pyparsing 3.3.2 pyhcf101f3_0 conda-forge Cached
+ pysocks 1.7.1 pyha55dd90_7 conda-forge Cached
+ python 3.14.5 habeac84_100_cp314 conda-forge 37MB
+ python-dateutil 2.9.0.post0 pyhe01879c_2 conda-forge Cached
+ python-dotenv 1.2.2 pyhcf101f3_0 conda-forge Cached
+ python-kaleido 0.2.1 pyhd8ed1ab_0 conda-forge Cached
+ python-multipart 0.0.29 pyhcf101f3_0 conda-forge 38kB
+ python_abi 3.14 8_cp314 conda-forge 7kB
+ pyyaml 6.0.3 py314h67df5f8_1 conda-forge 202kB
+ qhull 2020.2 h434a139_5 conda-forge Cached
+ re2 2025.11.05 h5301d42_1 conda-forge 27kB
+ readline 8.3 h853b02a_0 conda-forge Cached
+ requests 2.34.2 pyhcf101f3_0 conda-forge 69kB
+ rich 15.0.0 pyhcf101f3_0 conda-forge Cached
+ rich-argparse 1.8.0 pyhd8ed1ab_0 conda-forge 27kB
+ rich-click 1.9.8 pyh8f84b5b_0 conda-forge 64kB
+ rich-toolkit 0.19.10 pyhcf101f3_0 conda-forge 33kB
+ s2n 1.7.3 hc5a330e_0 conda-forge 388kB
+ seqkit 2.13.0 he881be0_0 bioconda Cached
+ seqtk 1.5 h577a1d6_1 bioconda 142kB
+ shellingham 1.5.4 pyhd8ed1ab_2 conda-forge Cached
+ six 1.17.0 pyhe01879c_1 conda-forge Cached
+ snappy 1.2.2 h03e3b7b_1 conda-forge Cached
+ sniffio 1.3.1 pyhd8ed1ab_2 conda-forge Cached
+ sqlite 3.53.1 hbc0de68_0 conda-forge 205kB
+ starlette 1.1.0 pyhcf101f3_0 conda-forge 64kB
+ tk 8.6.13 noxft_h366c992_103 conda-forge Cached
+ tomli 2.4.1 pyhcf101f3_0 conda-forge 22kB
+ tqdm 4.67.3 pyh8f84b5b_0 conda-forge Cached
+ typer 0.26.3 pyhcf101f3_0 conda-forge 184kB
+ typing-extensions 4.15.0 h396c80c_0 conda-forge Cached
+ typing-inspection 0.4.2 pyhcf101f3_2 conda-forge 21kB
+ typing_extensions 4.15.0 pyhcf101f3_0 conda-forge Cached
+ tzdata 2025c hc9c84f9_1 conda-forge Cached
+ unicodedata2 17.0.1 py314h5bd0f2a_0 conda-forge 410kB
+ upsetplot 0.9.0 pyhd8ed1ab_1 conda-forge 28kB
+ urllib3 2.7.0 pyhd8ed1ab_0 conda-forge 104kB
+ uvicorn 0.48.0 pyhc90fa1f_0 conda-forge 56kB
+ uvicorn-standard 0.48.0 he364bde_0 conda-forge 4kB
+ uvloop 0.22.1 py314h5bd0f2a_1 conda-forge 593kB
+ watchfiles 1.2.0 py314ha5689aa_0 conda-forge 416kB
+ websockets 16.0 py314h0f05182_1 conda-forge 383kB
+ xorg-libxau 1.0.12 hb03c661_1 conda-forge Cached
+ xorg-libxdmcp 1.1.5 hb03c661_1 conda-forge Cached
+ yaml 0.2.5 h280c20c_3 conda-forge Cached
+ zlib 1.3.2 h25fd6f3_2 conda-forge Cached
+ zlib-ng 2.3.3 hceb46e0_1 conda-forge Cached

Summary:

Install: 186 packages

Total download: 142MB

─────────────────────────────────────────────────────────────────────────────────────────────────

 

Transaction starting
libgrpc 7.0MB @ 2.3MB/s 3.0s
numpy 8.9MB @ 2.3MB/s 3.8s
matplotlib-base 8.5MB @ 2.0MB/s 4.2s
libarrow 6.5MB @ 2.3MB/s 2.8s
pandas 15.3MB @ 2.5MB/s 6.2s
libopenblas 5.9MB @ 2.3MB/s 2.5s
pyarrow-core 4.8MB @ 1.6MB/s 3.0s
libprotobuf 3.7MB @ 2.4MB/s 1.6s
aws-sdk-cpp 3.6MB @ 3.1MB/s 1.2s
biopython 3.4MB @ 2.0MB/s 1.7s
libgfortran5 2.5MB @ 2.6MB/s 1.0s
libgoogle-cloud 2.6MB @ 2.4MB/s 1.1s
pydantic-core 1.9MB @ 2.7MB/s 0.7s
libarrow-compute 3.0MB @ 1.9MB/s 1.6s
orc 1.5MB @ 2.8MB/s 0.5s
libparquet 1.4MB @ 3.1MB/s 0.5s
pip 1.2MB @ 2.9MB/s 0.4s
libabseil 1.4MB @ 2.2MB/s 0.6s
pillow 1.1MB @ 2.7MB/s 0.4s
libsqlite 955.0kB @ 2.9MB/s 0.3s
libgoogle-cloud-storage 779.6kB @ 2.7MB/s 0.3s
fonttools 846.0kB @ 2.1MB/s 0.4s
libopentelemetry-cpp 934.3kB @ 1.8MB/s 0.5s
libarrow-acero 592.3kB @ 2.2MB/s 0.2s
uvloop 593.4kB @ 1.3MB/s 0.4s
libarrow-dataset 592.2kB @ 2.7MB/s 0.2s
libarrow-substrait 501.9kB @ 1.8MB/s 0.2s
azure-storage-blobs-cpp 587.1kB @ 1.6MB/s 0.3s
libthrift 423.9kB @ 2.8MB/s 0.2s
crossroad 1.8MB @ 663.3kB/s 2.6s
libuv 419.9kB @ 2.3MB/s 0.2s
fastar 423.4kB @ 966.7kB/s 0.3s
aws-crt-cpp 412.5kB @ 2.9MB/s 0.1s
watchfiles 415.6kB @ 1.6MB/s 0.3s
unicodedata2 409.6kB @ 1.8MB/s 0.2s
libopentelemetry-cpp-headers 396.4kB @ 2.2MB/s 0.2s
s2n 388.1kB @ 2.5MB/s 0.1s
brotli-python 367.4kB @ 1.7MB/s 0.1s
websockets 383.0kB @ 1.3MB/s 0.3s
azure-core-cpp 348.7kB @ 2.7MB/s 0.1s
pydantic 346.5kB @ 1.9MB/s 0.2s
contourpy 324.0kB @ 2.3MB/s 0.1s
libpng 317.7kB @ 1.8MB/s 0.2s
azure-storage-files-datalake-cpp 303.8kB @ 1.9MB/s 0.1s
narwhals 284.3kB @ 1.8MB/s 0.2s
fontconfig 280.9kB @ 866.6kB/s 0.2s
python 36.7MB @ 3.0MB/s 12.0s
azure-identity-cpp 250.5kB @ 1.5MB/s 0.1s
lcms2 251.1kB @ 2.0MB/s 0.1s
aws-c-common 242.3kB @ 2.8MB/s 0.1s
libre2-11 213.1kB @ 66.4kB/s 0.1s
aws-c-http 230.3kB @ 1.7MB/s 0.1s
aws-c-mqtt 221.7kB @ 307.2kB/s 0.1s
sqlite 205.4kB @ ??.?MB/s 0.1s
perf_ssr 720.0kB @ 247.3kB/s 2.3s
prometheus-cpp 199.5kB @ 962.8kB/s 0.1s
pyyaml 202.4kB @ 1.6MB/s 0.1s
typer 184.4kB @ 1.9MB/s 0.1s
aws-c-io 181.6kB @ 1.9MB/s 0.1s
aws-c-s3 153.0kB @ 2.2MB/s 0.1s
azure-storage-common-cpp 159.1kB @ 1.8MB/s 0.1s
expat 148.2kB @ ??.?MB/s 0.0s
anyio 146.8kB @ 2.2MB/s 0.1s
glog 143.5kB @ 2.6MB/s 0.1s
seqtk 141.8kB @ 1.8MB/s 0.1s
nlohmann_json 136.2kB @ 2.1MB/s 0.1s
aws-c-auth 134.4kB @ 1.5MB/s 0.1s
certifi 134.2kB @ 1.8MB/s 0.1s
click 105.0kB @ 1.5MB/s 0.1s
gflags 119.7kB @ 148.2kB/s 0.1s
urllib3 103.6kB @ ??.?MB/s 0.0s
aws-checksums 101.6kB @ ??.?MB/s 0.0s
fastapi-core 95.5kB @ ??.?MB/s 0.0s
libmpdec 92.4kB @ ??.?MB/s 0.0s
packaging 91.6kB @ ??.?MB/s 0.0s
libutf8proc 86.0kB @ ??.?MB/s 0.0s
kiwisolver 77.4kB @ ??.?MB/s 0.0s
libexpat 77.3kB @ 885.4kB/s 0.1s
pydantic-extra-types 73.9kB @ ??.?MB/s 0.0s
markdown-it-py 69.0kB @ ??.?MB/s 0.0s
requests 68.7kB @ ??.?MB/s 0.0s
rich-click 64.4kB @ ??.?MB/s 0.0s
aws-c-event-stream 59.3kB @ ??.?MB/s 0.0s
starlette 63.7kB @ ??.?MB/s 0.0s
aws-c-sdkutils 59.1kB @ ??.?MB/s 0.0s
aws-c-cal 56.9kB @ ??.?MB/s 0.0s
idna 56.9kB @ ??.?MB/s 0.0s
uvicorn 56.3kB @ ??.?MB/s 0.0s
pydantic-settings 52.3kB @ ??.?MB/s 0.0s
email-validator 46.8kB @ ??.?MB/s 0.0s
libuuid 40.2kB @ ??.?MB/s 0.0s
h11 39.1kB @ ??.?MB/s 0.0s
python-multipart 37.8kB @ ??.?MB/s 0.0s
rich-toolkit 32.9kB @ ??.?MB/s 0.0s
upsetplot 28.0kB @ ??.?MB/s 0.0s
libstdcxx-ng 27.8kB @ ??.?MB/s 0.0s
libgfortran 27.7kB @ ??.?MB/s 0.0s
re2 27.5kB @ ??.?MB/s 0.0s
markupsafe 27.4kB @ ??.?MB/s 0.0s
pyarrow 26.8kB @ ??.?MB/s 0.0s
aws-c-compression 22.0kB @ ??.?MB/s 0.0s
tomli 21.6kB @ ??.?MB/s 0.0s
typing-inspection 20.9kB @ ??.?MB/s 0.0s
fastapi-cli 18.9kB @ ??.?MB/s 0.0s
libblas 18.8kB @ ??.?MB/s 0.0s
httptools 99.0kB @ ??.?MB/s 0.4s
liblapack 18.8kB @ ??.?MB/s 0.0s
libcblas 18.8kB @ ??.?MB/s 0.0s
email_validator 7.1kB @ ??.?MB/s 0.0s
backports.zstd 7.5kB @ ??.?MB/s 0.0s
python_abi 7.0kB @ ??.?MB/s 0.0s
fastapi 4.8kB @ ??.?MB/s 0.0s
uvicorn-standard 4.1kB @ ??.?MB/s 0.0s
rich-argparse 26.8kB @ ??.?MB/s 0.2s
plotly-upset-hd 356.0kB @ 181.5kB/s 1.8s
Linking seqkit-2.13.0-he881be0_0
Linking bedtools-2.31.1-h13024bc_3
Linking seqtk-1.5-h577a1d6_1
Linking libuuid-2.42.1-h5347b49_0
Linking readline-8.3-h853b02a_0
Linking libexpat-2.8.1-hecca717_0
Linking nspr-4.38-h29cc59b_0
Linking mathjax-2.7.7-ha770c72_3
Linking libuv-1.52.1-h280c20c_0
Linking yaml-0.2.5-h280c20c_3
Linking ld_impl_linux-64-2.45.1-default_hbd61a6d_102
Linking libmpdec-4.0.0-hb03c661_1
Linking libwebp-base-1.6.0-hd42ef1d_0
Linking zlib-ng-2.3.3-hceb46e0_1
Linking libstdcxx-ng-15.2.0-hdf11a46_19
Linking pthread-stubs-0.4-hb9d3cd8_1002
Linking xorg-libxau-1.0.12-hb03c661_1
Linking xorg-libxdmcp-1.1.5-hb03c661_1
Linking libgfortran5-15.2.0-h68bc16d_19
Linking libpng-1.6.58-h421ea60_0
Linking libbrotlicommon-1.2.0-hb03c661_1
Linking libjpeg-turbo-3.1.4.1-hb03c661_0
Linking libdeflate-1.25-h17f619e_0
Linking lerc-4.1.0-hdb68285_0
Linking libsqlite-3.53.1-h0c1763c_0
Linking libffi-3.5.2-h3435931_0
Linking tk-8.6.13-noxft_h366c992_103
Linking azure-core-cpp-1.16.2-h206d751_0
Linking libabseil-20260107.1-cxx17_h7b12aa8_0
Linking libutf8proc-2.11.3-hfe17d71_0
Linking libopentelemetry-cpp-headers-1.26.0-ha770c72_0
Linking zlib-1.3.2-h25fd6f3_2
Linking snappy-1.2.2-h03e3b7b_1
Linking nlohmann_json-3.12.0-h54a6638_1
Linking aws-c-common-0.13.1-hb03c661_0
Linking s2n-1.7.3-hc5a330e_0
Linking gflags-2.2.2-h5888daf_1005
Linking libevent-2.1.12-hf998b51_1
Linking expat-2.8.1-hecca717_0
Linking libcrc32c-1.1.2-h9c3ff4c_0
Linking qhull-2020.2-h434a139_5
Linking libxcb-1.17.0-h8a09558_0
Linking libgfortran-15.2.0-h69a702a_19
Linking libfreetype6-2.14.3-h73754d4_0
Linking libbrotlienc-1.2.0-hb03c661_1
Linking libbrotlidec-1.2.0-hb03c661_1
Linking libtiff-4.7.1-h9d88235_1
Linking sqlite-3.53.1-hbc0de68_0
Linking nss-3.118-h445c969_0
Linking azure-identity-cpp-1.13.3-hed0cdb0_1
Linking azure-storage-common-cpp-12.13.0-ha7a2c86_0
Linking libprotobuf-6.33.5-h6eeba95_1
Linking libre2-11-2025.11.05-h0dc7533_1
Linking prometheus-cpp-1.3.0-ha5d0236_0
Linking aws-c-compression-0.3.2-h16e98cb_1
Linking aws-checksums-0.2.10-h16e98cb_1
Linking aws-c-sdkutils-0.2.4-h16e98cb_5
Linking aws-c-cal-0.9.14-h8e43964_1
Linking glog-0.7.1-hbabe93e_0
Linking libthrift-0.22.0-h7d032f7_2
Linking libopenblas-0.3.33-pthreads_h94d23a6_0
Linking libfreetype-2.14.3-ha770c72_0
Linking brotli-bin-1.2.0-hb03c661_1
Linking lcms2-2.19.1-h0c24ade_0
Linking openjpeg-2.5.4-h55fea9a_0
Linking azure-storage-blobs-cpp-12.17.0-hf824e48_1
Linking re2-2025.11.05-h5301d42_1
Linking aws-c-io-0.26.3-h955231c_3
Linking libblas-3.11.0-8_h4a7cf45_openblas
Linking fontconfig-2.18.0-h27c8c51_0
Linking freetype-2.14.3-ha770c72_0
Linking brotli-1.2.0-hed03a55_1
Linking azure-storage-files-datalake-cpp-12.15.0-h1e5b466_0
Linking libgrpc-1.78.1-h1d1128b_0
Linking aws-c-event-stream-0.7.1-h9be7a74_1
Linking aws-c-http-0.11.0-hcbcd92d_1
Linking libcblas-3.11.0-8_h0358290_openblas
Linking liblapack-3.11.0-8_h47877c9_openblas
Linking libopentelemetry-cpp-1.26.0-h9692893_0
Linking aws-c-auth-0.10.3-h3aafcba_1
Linking aws-c-mqtt-0.15.2-h8af55cf_3
Linking libgoogle-cloud-3.5.0-h25dbb67_0
Linking aws-c-s3-0.12.3-h00bea6e_2
Linking libgoogle-cloud-storage-3.5.0-hdbdcf42_0
Linking aws-crt-cpp-0.38.3-h7b0d4b4_2
Linking aws-sdk-cpp-1.11.747-h5a171d8_5
Linking python_abi-3.14-8_cp314
Linking font-ttf-dejavu-sans-mono-2.37-hab24e00_0
Linking tzdata-2025c-hc9c84f9_1
Linking font-ttf-ubuntu-0.83-h77eed37_3
Linking font-ttf-inconsolata-3.000-h77eed37_0
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Linking libarrow-24.0.0-h6f10b76_3_cpu
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Linking libarrow-compute-24.0.0-h53684a4_3_cpu
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Linking libarrow-dataset-24.0.0-h635bf11_3_cpu
Linking libarrow-substrait-24.0.0-hb4dd7c2_3_cpu
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Transaction finished

(jitENV) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$ crossroad -h

Usage: crossroad [OPTIONS]

Run the main croSSRoad analysis pipeline, or manage jobs.

╭─ Options ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --version -v Show version, logo, citation, and links. │
│ --install-completion Install completion for the current shell. │
│ --show-completion Show completion for the current shell, to copy it or customize the installation. │
│ --help -h Show this message and exit. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Mode Selection ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --api -a Run the Crossroad web API server. │
│ --slurm -s Submit the analysis job to a Slurm cluster. │
│ --job-status JOB_ID Query the status of a specific job ID. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Input Files (provide --input-dir OR --fasta) ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --input-dir -i PATH Directory containing: `all_genome.fa`, ``, ``. Exclusive with `--fasta`. │
│ --fasta -fa PATH Input FASTA file (e.g., `all_genome.fa`). Alternative to `--input-dir`. │
│ --categories -c PATH Genome categories TSV file. Optional if using `--fasta`. Ignored if `--input-dir` is used (looks for `genome_categories.tsv` inside). │
│ --gene-bed -b PATH Gene BED file for SSR-gene analysis. Optional. If `--input-dir` is used, looks for `gene.bed` inside. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Analysis Parameters ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --reference-id -ref TEXT Reference genome ID for comparative analysis. Optional parameter for reference-based comparisons. │
│ --output-dir -o DIRECTORY Base output directory for jobs. Overrides CROSSROAD_JOB_DIR env var. │
│ --flanks -f Process flanking regions. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ PERF SSR Detection Parameters ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --mono INTEGER Mononucleotide repeat threshold. [default: 12] │
│ --di INTEGER Dinucleotide repeat threshold. [default: 6] │
│ --tri INTEGER Trinucleotide repeat threshold. [default: 4] │
│ --tetra INTEGER Tetranucleotide repeat threshold. [default: 3] │
│ --penta INTEGER Pentanucleotide repeat threshold. [default: 3] │
│ --hexa INTEGER Hexanucleotide repeat threshold. [default: 2] │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Filtering Parameters ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --min-len -l INTEGER Minimum genome length for filtering. [default: 1000] │
│ --max-len -L INTEGER Maximum genome length for filtering. [default: 10000000] │
│ --unfair -u INTEGER Maximum number of N's allowed per genome for Crossroad analysis. [default: 0] │
│ --repeat-threshold -rc INTEGER Repeat count Threshold for hotspot filtering (keeps records > this value). [default: 1] │
│ --genome-threshold -g INTEGER Genome count Threshold for hotspot filtering (keeps records > this value). [default: 2] │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Performance & Output ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --threads -t INTEGER Number of threads for Crossroad analysis. [default: 50] │
│ --plots -p Enable plot generation. │
│ --intrim-dir TEXT Name for the intermediate files directory (within the main job output dir). [default: intrim] │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

(jitENV) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$

 

]]> ComBioX https://bioinformaticsonline.com/blog/view/45316/from-genes-to-algorithms-the-ai-revolution-in-bioinformatics Wed, 16 Sep 2026 02:23:40 -0500 https://bioinformaticsonline.com/blog/view/45316/from-genes-to-algorithms-the-ai-revolution-in-bioinformatics <![CDATA[From Genes to Algorithms: The AI Revolution in Bioinformatics]]> Imagine waking up to a message from your doctor saying your genetic profile has been analyzed and a small change in your DNA has been found that could raise your risk for a certain disease. Instead of being alarmed, you learn that the condition was caught early and your genetic information can help doctors find treatments that might work better for you. Not long ago, this would have seemed like science fiction. But thanks to the rapid progress of artificial intelligence (AI) and bioinformatics, it is becoming more realistic. Bioinformatics brings together biology, computer science, mathematics, and statistics to collect, manage, and analyze biological data. As new technologies produce huge amounts of genomic, transcriptomic, proteomic, and clinical data, traditional methods often cannot keep up. AI is now playing a bigger role in bioinformatics, helping researchers find patterns, make predictions, and understand biological systems in ways that were once out of reach.

Modern sequencing technologies have turned biology into a data-heavy science. The human genome has about three billion DNA base pairs, and sequencing can produce massive datasets quickly. Bioinformatics helps organize and interpret this information, allowing scientists to compare genomes, find genes, spot genetic differences, study gene expression, and understand diseases at the molecular level. Now, AI is changing how these datasets are analyzed. Machine learning and deep learning can find complex patterns in biological data and make predictions using large datasets. Rather than following only set rules, AI systems can learn from data and help researchers solve tough biological problems.

A key example of AI in bioinformatics is predicting protein structures. Proteins are vital for many functions in living things, and their three-dimensional shapes are closely tied to what they do. For a long time, figuring out these structures in the lab was slow and difficult. AI-based tools like AlphaFold have shown that deep learning can predict protein structures with impressive accuracy. These advances help speed up biological research and give scientists useful predictions to support their experiments. This shift means bioinformatics is moving from just analyzing biological data to predicting how biology works and designing new solutions.

AI is also expected to greatly influence drug discovery. Traditionally, developing a new drug is a long and costly process that involves finding biological targets, searching for possible drug molecules, testing how well they work, studying their properties, and running many experiments. AI can help researchers analyze large chemical and biological datasets, predict how molecules might interact with targets, find promising compounds, and estimate properties of drug candidates. Generative AI can even help design new molecules to test in the lab. This does not mean lab experiments will go away, but AI can help researchers focus on the best options and possibly speed up some parts of drug development.

Personalized medicine is another area where AI-driven bioinformatics could make a big difference. Each person has a unique mix of genetic, molecular, environmental, and lifestyle factors, so people with the same disease might respond differently to the same treatment. Bioinformatics lets researchers study individual biological data, and AI can combine many types of information, like genetic data, gene expression, protein levels, medical images, clinical records, and drug responses. In the future, this could help doctors look beyond just the disease and focus on the specific biology of each patient. This would support more personalized ways to diagnose, assess risk, and choose treatments.

As AI becomes more common, the job of bioinformaticians will likely change. Instead of spending most of their time on repetitive computer tasks, they may focus more on creating research questions, understanding AI results, checking predictions, and linking computer findings to lab experiments. Future professionals will need to know about molecular biology, genetics, statistics, programming, machine learning, data science, databases, and computational biology. But technical skills alone will not be enough. Being able to ask good scientific questions and carefully judge computer results will be even more important, since AI can make predictions, but scientists must decide if those predictions make sense and can be proven in the lab.

Even with its great potential, using AI in bioinformatics comes with big challenges. AI systems rely on the quality and variety of the data they are trained on. Biological and clinical datasets can have missing information, technical errors, inconsistent measurements, and may not represent all groups equally. A model that works well on one dataset might not work as well on another group or in a different setting. That is why data quality, proper validation, transparency, reproducibility, and careful model development are crucial for the future of AI in bioinformatics. Researchers also need to think about genetic privacy, data ownership, security, and using personal biological information responsibly. These concerns are especially important when AI predictions are used in healthcare.

The lab of the future may look very different from today’s. Researchers might use AI to review scientific papers, find research questions, analyze large datasets, come up with ideas, and predict which experiments will be most helpful. Automated lab systems could then run these experiments, create new data, and send the results back to computer models. This would create a cycle of making guesses, predicting, experimenting, collecting data, and learning. This approach could speed up scientific discovery by helping researchers explore questions more efficiently. Bioinformatics could become a key link between computer predictions and real lab experiments.

In the end, the future of bioinformatics with AI will not be about replacing people with machines. It will be about humans and AI working together. Computers can handle huge amounts of biological data and spot patterns that a single person could not. But humans bring scientific understanding, creativity, critical thinking, ethical judgment, and the ability to see the bigger picture. AI might find a pattern in millions of genomes, but scientists need to figure out why it matters. An AI model could suggest a new drug, but researchers must check if it is safe and works well. A computer might find a genetic change, but doctors have to decide what it means for a real patient.

The future of bioinformatics is closely tied to the future of AI. As biological data grows and AI models get better, bioinformatics may shift from mainly analyzing data to predicting biological processes, designing new molecules, speeding up experiments, and supporting personalized healthcare. For students and researchers, this is an exciting chance to work where biology and technology meet. The most successful people will not just be those who use the latest AI tools, but those who understand biology well enough to ask good questions and judge the answers carefully. The future will not be about AI versus humans, but about people and AI working together to understand life in ways we never could before. The next big discovery may come from this partnership between a biological question, a computer model, a lab experiment, and human curiosity.

]]> Jitendra Narayan https://bioinformaticsonline.com/news/view/1973/webinar-wednesday-21-august-2013-at-noon-edt Sun, 11 Aug 2013 19:31:56 -0500 https://bioinformaticsonline.com/news/view/1973/webinar-wednesday-21-august-2013-at-noon-edt <![CDATA[Webinar: Wednesday 21 August 2013 at Noon EDT]]> This webinar will describe the use of combinatorial pooling to reconstruct gene sequences within BACs. Recent work in barley has shown that this level of sequence knowledge is sufficient to support critical end-point objectives such as map-based cloning and marker-assisted breeding.

http://www.extension.org/pages/67926/upcoming-webinar:-selective-sequencing-through-combinatorial-pooling#.UggsVuHyPqU

]]> Jitendra Narayan https://bioinformaticsonline.com/opportunity/view/10392/research-associate-ra-at-institute-of-advanced-study-in-science-and-technology Mon, 05 May 2014 08:44:24 -0500 <![CDATA[Research Associate (RA) at INSTITUTE OF ADVANCED STUDY IN SCIENCE AND TECHNOLOGY]]> INSTITUTE OF ADVANCED STUDY IN SCIENCE AND TECHNOLOGY
(An Autonomous Institute under Department of Science and Technology, Govt. of India)
Paschim Boragaon, Garchuk, Guwahati-781035

Appointment Adv.No.2

Applications in plain paper are invited from Indian citizens for one/two position each of Research Associate, Traineeship and Studentship for BIF facility, Division of Life Sciences, IASST.

Applications with complete Bio-data containing contact address, e-mail and phone number, two recent passport size photographs and attested copies of mark sheets, certificates etc., should be sent to the Registrar, IASST, Paschim Boragaon, Garchuk, Guwahati – 781035, Assam, so as to reach on or before 5/05/2014.

A. Research Associate:

Number of vacancies: 1 (One)

Qualifications:

PhD in Bioinformatics or allied disciplines with knowledge of Bioinformatics. The candidates who have submitted PhD thesis may also apply.

In case, candidates having PhD are not found, candidates having MSc in Bioinformatics or allied disciplines with sound knowledge of Bioinformatics will be preferred.

Remuneration: Candidate having PhD will get a consolidated remuneration of Rs. 22,000/- +HRA per month. MSc having NET/GATE/SLET qualified candidate will get a remuneration of Rs. 16,000/= and HRA and candidate with only MSc will get a remuneration of Rs.14,000/- and HRA.

Tenure:

The post is initially for one year and may be extended depending on the performance till the tenure of the project.

B. Traineeship:

Number of vacancies: 2 (Two)

Qualifications:

Candidate with a postgraduate degree in Bioinformatics/Biotechnology/Life sciences from a recognised University

Remuneration: Rs. 5000/month for 6 months

C. Studentship:

Number of vacancies: 2 (Two)

Qualifications:

Candidate pursuing M.Sc in bioinformatics in a recognised University.

Remuneration: Rs. 5000/month for 6 months

Advertisement:

http://iasst.gov.in/pdf/recruitment/advt%20no_2_24042014.pdf

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