https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkae679/7730539?login=true

Address of the bookmark: https://github.com/Zhenlisme/heliano/

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
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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$

SyRI, a pairwise whole-genome comparison tool for chromosome-level assemblies. SyRI starts by finding rearranged regions and then searches for differences in the sequences, which are distinguished for residing in syntenic or rearranged regions. This distinction is important as rearranged regions are inherited differently compared to syntenic regions.

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1911-0

Address of the bookmark: https://github.com/schneebergerlab/syri

Address of the bookmark: https://bioconductor.org/packages/release/bioc/html/doubletrouble.html

http://bioinformaticsonline.com/videolist/watch/19555/a-3d-map-of-the-human-genome

Researchers have been working to determine how cells regulate gene expression for nearly as long as we’ve known about DNA. How, for example, do nerve cells know to turn off only nerve cell genes and turn off bone cell genes? DNA folding loops are part of the answer. This research team, which published their findings in a paper in Cell http://www.cell.com/cell/abstract/S0092-8674%2814%2901497-4 , found that the number of loops is much lower than expected. There are only 10,000 loops instead of the predicted millions, and they form on/off switches in DNA.

More at http://www.eurekalert.org/pub_releases/2014-12/ru-3mr121114.php

Reference http://www.cell.com/cell/abstract/S0092-8674%2814%2901497-4

We have sequenced the CHM13hTERT human cell line with a number of technologies. Human genomic DNA was extracted from the cultured cell line. As the DNA is native, modified bases will be preserved. The data includes 30x PacBio HiFi, 120x coverage of Oxford Nanopore, 70x PacBio CLR, 50x 10X Genomics, as well as BioNano DLS and Arima Genomics HiC. Most raw data is available from this site, with the exception of the PacBio data which was generated by the University of Washington/PacBio and is available from NCBI SRA.

A UCSC browser is available for v2.0 (as well as legacy v1.0 and v1.1 versions). An interactive dotplot visualization of all genomic repeats is also available from resgen.io. Known issues identified in the assembly are tracked at CHM13 issues.

MORE at https://github.com/marbl/CHM13

Address of the bookmark: https://www.science.org/doi/10.1126/science.abj6987

TRRUST database also provides information of mode of regulation (activation or repression). Currently 8,972 (59.8%) regulatory relationships are known for mode of regulation.

Search at : http://www.grnpedia.org/trrust/Network_search_form.php

Address of the bookmark: http://www.grnpedia.org/trrust/

Scientists have reconstructed the genome of an ancient human who lived nearly 5,700 years ago in Southern Denmark from the birch pitch- an ancient tar-like substance.

By sequencing the sample, researchers not only discovered the ancient human DNA but also microbial DNA reflecting the oral microbiome of the person who chewed the pitch, along with plant and animal DNA that could be the recent meal she might have consumed.

The DNA sample is comparable in quality to well-preserved teeth and skull bones. The DNA suggests that the chewer was a female, most likely with dark skin, dark brown hair and blue eyes.

What Are Chromosome Rearrangements?

Chromosome rearrangements are structural changes that alter the normal configuration of chromosomes. These changes can involve large segments of DNA — from thousands to millions of base pairs — and can occur spontaneously, be inherited, or result from exposure to mutagens (like radiation or chemicals).

Common Types of Rearrangements:

1. Deletions – Loss of a chromosome segment

2. Duplications – Repetition of a segment

3. Inversions – A segment breaks off, flips, and reattaches

4. Translocations – Segments exchange places between non-homologous chromosomes

5. Insertions – A segment is inserted into another part of the genome

These changes can disrupt genes directly or affect gene regulation, leading to disease.

How Do Chromosome Rearrangements Cause Disease?

The impact of a rearrangement depends on which genes are involved, how much DNA is affected, and when the rearrangement occurs (in development vs. adulthood). Here are some key mechanisms:

• Gene disruption: Breaking a gene can lead to loss of function or the creation of a non-functional protein.

• Gene fusion: Joining parts of two genes may form a novel hybrid gene with new functions (common in cancer).

• Dosage effects: Extra or missing gene copies can disturb the balance of gene expression.

• Position effects: Moving a gene to a new regulatory environment may silence or over-activate it.

Chromosome Rearrangements in Human Disease

1. Developmental Disorders

• Cri-du-chat syndrome: Caused by a deletion on chromosome 5p. Affected infants often have a high-pitched cry and intellectual disability.

• Williams syndrome: Results from a microdeletion on chromosome 7q, affecting genes related to cardiovascular and cognitive function.

2. Cancer

Cancer is perhaps the most striking example of disease caused by chromosome rearrangements.

• Chronic Myeloid Leukemia (CML): Caused by a translocation between chromosomes 9 and 22, forming the Philadelphia chromosome. This creates the BCR-ABL fusion gene, which drives uncontrolled cell growth.

• Burkitt lymphoma: Involves translocation of the MYC gene, leading to excessive cell division.

• Ewing sarcoma: A fusion of EWSR1 and FLI1 genes through translocation promotes tumor development.

3. Infertility and Miscarriages

Balanced rearrangements (like inversions or translocations) in carriers may not cause disease directly but can result in:

• Recurrent miscarriages

• Infertility

• Birth defects in offspring

Detecting Rearrangements

Thanks to modern genomics, chromosome rearrangements can now be detected with high precision using:

• Karyotyping – Classic method for detecting large rearrangements

• FISH (Fluorescence In Situ Hybridization) – Uses fluorescent probes to target specific DNA sequences

• Array CGH (Comparative Genomic Hybridization) – Detects copy number changes across the genome

• Whole Genome Sequencing (WGS) – Identifies even small or complex rearrangements at base-pair resolution

Looking Forward: The Future of Chromosome Medicine

Understanding chromosome rearrangements is now central to:

• Personalized medicine

• Genetic counseling

• Targeted therapies, especially in cancer (e.g., tyrosine kinase inhibitors for BCR-ABL fusion)

With the rise of long-read sequencing and single-cell genomics, even previously “invisible” rearrangements are being uncovered, offering new insights into both rare diseases and common conditions.

Final Thoughts

Chromosome rearrangements remind us that genetics isn't just about which genes we have — but where they are, how they're arranged, and when they're active. As our tools grow sharper, so does our ability to diagnose, understand, and treat diseases rooted in genomic architecture.

In a way, the genome is like a book not just defined by its words, but also by how the chapters are ordered. Rearranging them can create a new story — sometimes harmful, sometimes insightful — and understanding these changes is key to writing a healthier future.

Source: Life technologies

Address of the bookmark: http://www.lifetechnologies.com/global/en/home/communities-social/blog/blogs/sequencing-solutions-to-world-health.html?cid=social_blogseries_20130829_11098264