<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/40465?offset=430 https://bioinformaticsonline.com/bookmarks/view/43902/interactivenn-a-web-based-tool-for-the-analysis-of-sets-through-venn-diagrams Wed, 29 Jun 2022 03:22:26 -0500 https://bioinformaticsonline.com/bookmarks/view/43902/interactivenn-a-web-based-tool-for-the-analysis-of-sets-through-venn-diagrams <![CDATA[InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams]]> InteractiVenn, a more flexible tool for interacting with Venn diagrams including up to six sets. It offers a clean interface for Venn diagram construction and enables analysis of set unions while preserving the shape of the diagram. Set unions are useful to reveal differences and similarities among sets and may be guided in our tool by a tree or by a list of set unions. The tool also allows obtaining subsets’ elements, saving and loading sets for further analyses, and exporting the diagram in vector and image formats. InteractiVenn has been used to analyze two biological datasets, but it may serve set analysis in a broad range of domains.

More at https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0611-3

image

Address of the bookmark: http://www.interactivenn.net/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/44641/heliano-a-fast-and-accurate-tool-for-detection-of-helitron-like-elements Tue, 13 Aug 2024 07:16:34 -0500 https://bioinformaticsonline.com/bookmarks/view/44641/heliano-a-fast-and-accurate-tool-for-detection-of-helitron-like-elements <![CDATA[HELIANO: A fast and accurate tool for detection of Helitron-like elements]]> Helitron-like elements (HLE1 and HLE2) are DNA transposons. They have been found in diverse species and seem to play significant roles in the evolution of host genomes. Although known for over twenty years, Helitron sequences are still challenging to identify. Here, we propose HELIANO (Helitron-like elements annotator) as an efficient solution for detecting Helitron-like elements.

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

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

]]> LEGE 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
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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
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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
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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/news/view/4590/tigers-genome-sequenced Tue, 17 Sep 2013 16:48:24 -0500 https://bioinformaticsonline.com/news/view/4590/tigers-genome-sequenced <![CDATA[Tigers genome sequenced]]> Fifteen scientists led by Dr Jong Bhak of Genome Research Foundation, South Korea, decoded as many as 3 billion nucleotides (organic molecules that form the basic building blocks of nucleic acids, such as DNA). They identified 20,000 genes related to various functions of the tiger. 

The biggest and perhaps most fearsome of the world's big cats, the tiger, shares 95.6 percent of its DNA with humans' cute and furry companions, domestic cats.

The new research showed that big cats have genetic mutations that enabled them to be carnivores. The team also identified mutations that allow snow leopards to thrive at high altitudes.

Reference:

http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690

http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms

Paper:

http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html

]]> Rahul Agarwal https://bioinformaticsonline.com/bookmarks/view/34443/opera-an-optimal-genome-scaffolding-program Mon, 27 Nov 2017 10:18:20 -0600 https://bioinformaticsonline.com/bookmarks/view/34443/opera-an-optimal-genome-scaffolding-program <![CDATA[Opera: An optimal genome scaffolding program]]> Opera (Optimal Paired-End Read Assembler) is a sequence assembly program (http://en.wikipedia.org/wiki/Sequence_assembly image). It uses information from paired-end or long reads to optimally order and orient contigs assembled from shotgun-sequencing reads.

An updated version called OPERA-LG has been re-engineered with features for the assembly of large and complex genomes.

Song Gao, Denis Bertrand, Burton K. H. Chia and Niranjan Nagarajan. OPERA-LG: efficient and exact scaffolding of large, repeat-rich eukaryotic genomes with performance guarantees. Genome Biology, May 2016, doi: 10.1186/s13059-016-0951-y.

Song Gao, Wing-Kin Sung, Niranjan Nagarajan. Opera: reconstructing optimal genomic scaffolds with high-throughput paired-end sequences. Journal of Computational Biology, Sept. 2011, doi:10.1089/cmb.2011.0170.

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0951-y

Address of the bookmark: https://sourceforge.net/projects/operasf/

]]> Jit https://bioinformaticsonline.com/pages/view/34418/spades-hybrid-genome-assembly Mon, 27 Nov 2017 08:05:40 -0600 https://bioinformaticsonline.com/pages/view/34418/spades-hybrid-genome-assembly <![CDATA[SPAdes hybrid genome assembly]]> When you have both Illumina and Nanopore data, then SPAdes remains a good option for hybrid assembly - SPAdes was used to produce the B fragilis assembly by Mick Watson’s group.

Again, running spades.py will show you the options:

spades.py

This produces:

SPAdes genome assembler v3.10.1

Usage: /usr/local/SPAdes-3.10.1-Linux/bin/spades.py [options] -o <output_dir>

Basic options:
-o      <output_dir>    directory to store all the resulting files (required)
--sc                    this flag is required for MDA (single-cell) data
--meta                  this flag is required for metagenomic sample data
--rna                   this flag is required for RNA-Seq data
--plasmid               runs plasmidSPAdes pipeline for plasmid detection
--iontorrent            this flag is required for IonTorrent data
--test                  runs SPAdes on toy dataset
-h/--help               prints this usage message
-v/--version            prints version

Input data:
--12    <filename>      file with interlaced forward and reverse paired-end reads
-1      <filename>      file with forward paired-end reads
-2      <filename>      file with reverse paired-end reads
-s      <filename>      file with unpaired reads
--pe<#>-12      <filename>      file with interlaced reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-1       <filename>      file with forward reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-2       <filename>      file with reverse reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-s       <filename>      file with unpaired reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-<or>    orientation of reads for paired-end library number <#> (<#> = 1,2,..,9; <or> = fr, rf, ff)
--s<#>          <filename>      file with unpaired reads for single reads library number <#> (<#> = 1,2,..,9)
--mp<#>-12      <filename>      file with interlaced reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-1       <filename>      file with forward reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-2       <filename>      file with reverse reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-s       <filename>      file with unpaired reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-<or>    orientation of reads for mate-pair library number <#> (<#> = 1,2,..,9; <or> = fr, rf, ff)
--hqmp<#>-12    <filename>      file with interlaced reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-1     <filename>      file with forward reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-2     <filename>      file with reverse reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-s     <filename>      file with unpaired reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-<or>  orientation of reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9; <or> = fr, rf, ff)
--nxmate<#>-1   <filename>      file with forward reads for Lucigen NxMate library number <#> (<#> = 1,2,..,9)
--nxmate<#>-2   <filename>      file with reverse reads for Lucigen NxMate library number <#> (<#> = 1,2,..,9)
--sanger        <filename>      file with Sanger reads
--pacbio        <filename>      file with PacBio reads
--nanopore      <filename>      file with Nanopore reads
--tslr  <filename>      file with TSLR-contigs
--trusted-contigs       <filename>      file with trusted contigs
--untrusted-contigs     <filename>      file with untrusted contigs

Pipeline options:
--only-error-correction runs only read error correction (without assembling)
--only-assembler        runs only assembling (without read error correction)
--careful               tries to reduce number of mismatches and short indels
--continue              continue run from the last available check-point
--restart-from  <cp>    restart run with updated options and from the specified check-point ('ec', 'as', 'k<int>', 'mc')
--disable-gzip-output   forces error correction not to compress the corrected reads
--disable-rr            disables repeat resolution stage of assembling

Advanced options:
--dataset       <filename>      file with dataset description in YAML format
-t/--threads    <int>           number of threads
                                [default: 16]
-m/--memory     <int>           RAM limit for SPAdes in Gb (terminates if exceeded)
                                [default: 250]
--tmp-dir       <dirname>       directory for temporary files
                                [default: <output_dir>/tmp]
-k              <int,int,...>   comma-separated list of k-mer sizes (must be odd and
                                less than 128) [default: 'auto']
--cov-cutoff    <float>         coverage cutoff value (a positive float number, or 'auto', or 'off') [default: 'off']
--phred-offset  <33 or 64>      PHRED quality offset in the input reads (33 or 64)
                                [default: auto-detect]

As you can see this is also a “pipeline” of tools that can be switched on or off. SPAdes takes quite a long time, so for the purposes of this practical, something like this may suffice:

spades.py -t 4 \
          -m 32 \
          -k 31,51,71 \
          --only-assembler \
          -1 miseq.1.fastq -2 miseq.2.fastq \
          --nanopore minion.fastq \
          -o hybrid_assembly

In turn, these parameters mean

  • use 4 threads
  • max memory is 32Gb
  • use 3 kmer values to build the de bruijn graph(s) - 31, 51 and 71
  • only run the assembler, not the correction algorithm (for speed)
  • read 1 and read 2 of the MiSeq data
  • the nanopore data
  • put the output in folder “hybrid_assembly”
]]> Jit https://bioinformaticsonline.com/pages/view/34685/tools-for-bacterial-whole-genome-annotation Sat, 16 Dec 2017 17:37:47 -0600 https://bioinformaticsonline.com/pages/view/34685/tools-for-bacterial-whole-genome-annotation <![CDATA[Tools for bacterial whole genome annotation]]> RAST – Web tool (upload contigs), uses the subsystems in the SEED database and provides detailed annotation and pathway analysis. Takes several hours per genome but I think this is the best way to get a high quality annotation (if you have only a few genomes to annotate).

Prokka – Standalone command line tool, takes just a few minutes per genome. This is the best way to get good quality annotation in a flash, which is particularly useful if you have loads of genomes or need to annotate a pangenome or metagenome. Note however that the quality of functional information is not as good as RAST, and you will need several extra steps if you want to do functional profiling and pathway analysis of your genome(s)… which is in-built in RAST.

NCBI Prokaryotic Genome Annotation Pipeline is designed to annotate bacterial and archaeal genomes (chromosomes and plasmids).

Genome annotation is a multi-level process that includes prediction of protein-coding genes, as well as other functional genome units such as structural RNAs, tRNAs, small RNAs, pseudogenes, control regions, direct and inverted repeats, insertion sequences, transposons and other mobile elements.

PGAP: NCBI has developed an automatic prokaryotic genome annotation pipeline that combines ab initio gene prediction algorithms with homology based methods. The first version of NCBI Prokaryotic Genome Automatic Annotation Pipeline (PGAAP; see Pubmed Article) developed in 2005 has been replaced with an upgraded version that is capable of processing a larger data volume.  NCBI's annotation pipeline depends on several internal databases and is not currently available for download or use outside of the NCBI environment.

BEACON (automated tool for Bacterial GEnome Annotation ComparisON), a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at: http://www.cbrc.kaust.edu.sa/BEACON/.

BlastKOLA: Assigns K numbers to the user's sequence data by BLAST searches, respectively, against a nonredundant set of KEGG GENES. KOALA (KEGG Orthology And Links Annotation) is KEGG's internal annotation tool for K number assignment of KEGG GENES using SSEARCH computation. Annotate Sequence in KEGG Mapper and Pathogen Checker in KEGG Pathogen are special interfaces to this server and can be executed in an interactive mode. BlastKOALA is suitable for annotating fully sequenced genomes.

PAGIT: Provides a toolkit for improving the quality of genome assemblies created via an assembly software. PAGIT compiled four tools: (i) ABACAS which classifies and orientates contigs and estimates the sizes of gaps between them; (ii) IMAGE uses paired-end reads to extend contigs and close gaps within the scaffolds; (iii) ICORN for identifying and correcting small errors in consensus sequences and; (iv) RATT for help annotation. The software was mainly created to analyze parasite genomes of up to about 300 Mb.

MAKER: A portable and easily configurable genome annotation pipeline. MAKER allows smaller eukaryotic and prokaryotic genome projects to independently annotate their genomes and to create genome databases. It identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values. MAKER's inputs are minimal and its ouputs can be directly loaded into a Generic Model Organism Database (GMOD). They can also be viewed in the Apollo genome browser; this feature of MAKER provides an easy means to annotate, view and edit individual contigs and BACs without the overhead of a database. MAKER is available for download and can be tested online via the MAKER Web Annotation Service (MWAS).

MyPro is a software pipeline for high-quality prokaryotic genome assembly and annotation. It was validated on 18 oral streptococcal strains to produce submission-ready, annotated draft genomes. MyPro installed as a virtual machine and supported by updated databases will enable biologists to perform quality prokaryotic genome assembly and annotation with ease.

]]> Radha Agarkar https://bioinformaticsonline.com/bookmarks/view/34867/magic-blast-a-tool-for-mapping-large-next-generation-rna-or-dna-sequencing-runs-against-a-whole-genome-or-transcriptome Tue, 26 Dec 2017 22:23:39 -0600 https://bioinformaticsonline.com/bookmarks/view/34867/magic-blast-a-tool-for-mapping-large-next-generation-rna-or-dna-sequencing-runs-against-a-whole-genome-or-transcriptome <![CDATA[Magic-BLAST: a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome.]]> Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome. Each alignment optimizes a composite score, taking into account simultaneously the two reads of a pair, and in case of RNA-seq, locating the candidate introns and adding up the score of all exons. This is very different from other versions of BLAST, where each exon is scored as a separate hit and read-pairing is ignored.

Magic-BLAST incorporates within the NCBI BLAST code framework ideas developed in the NCBI Magic pipeline, in particular hit extensions by local walk and jump (http://www.ncbi.nlm.nih.gov/pubmed/26109056), and recursive clipping of mismatches near the edges of the reads, which avoids accumulating artefactual mismatches near splice sites and is needed to distinguish short indels from substitutions near the edges.

Address of the bookmark: https://ncbi.github.io/magicblast/

]]> Jit https://bioinformaticsonline.com/pages/view/35429/list-of-visualization-tools-for-genome-alignments Fri, 02 Feb 2018 13:25:33 -0600 https://bioinformaticsonline.com/pages/view/35429/list-of-visualization-tools-for-genome-alignments <![CDATA[List of visualization tools for genome alignments]]> Genome browsers are useful not only for showing final results but also for improving analysis protocols, testing data quality, and generating result drafts. Its integration in analysis pipelines allows the optimization of parameters, which leads to better results. But sometime, we need publication ready figure of genomes. Following are the list of genome alignment visualization tools, which could be useful for analysis and interpretation of results:

ABySS Explorer

Interactive Java application that uses a novel graph-based representation to display a sequence assembly and associated metadata

http://www.bcgsc.ca/platform/bioinfo/software/abyss-explorer

BamView

Genome browser and annotation tool that allows visualization of sequence features, next-generation sequencing (NGS) data and the results of analyses within the context of the sequence, and also its six-frame translation

http://www.sanger.ac.uk/resources/software/artemis/

DNannotator 

Annotation web toolkit for regional genomic sequences

http://bioapp.psych.uic.edu/DNannotator.htm

JVM 

Java Visual Mapping tool for NGS reads

http://www.springer.com/cda/content/document/cda_downloaddocument/9789401792448-c2.pdf?SGWID=0-0-45-1487072-p176815501

LookSeq 

Web-based visualization of sequences derived from multiple sequencing technologies. Low- or high-depth read pileups and easy visualization of putative single nucleotide and structural variation

http://lookseq.sourceforge.net

MagicViewer 

Visualization of short read alignment, identification of genetic variation and association with annotation information of a reference genome

http://bioinformatics.zj.cn/magicviewer/

MapView 

Alignments of huge-scale single-end and pair-end short reads

http://omictools.com/mapview-s1367.html

MultiPipMaker

Computes alignments of similar regions in two DNA sequences. The resulting alignments are summarized with a ‘percent identity plot’ (pip)

http://pipmaker.bx.psu.edu/pipmaker/

PileLineGUI 

Handling genome position files in NGS studies

http://sing.ei.uvigo.es/pileline/pilelinegui.html

SAMtools tview 

Simple and fast text alignment viewer; NGS compatible

http://www.htslib.org/

SEWAL

Uses a locality-sensitive hashing algorithm to enumerate all unique sequences in an entire Illumina sequencing run

http://www.sourceforge.net/projects/sewal

STAR 

A web-based integrated solution to management and visualization of sequencing data

http://wanglab.ucsd.edu/star/browser

SVA 

Software for annotating and visualizing sequenced human genomes

http://www.svaproject.org

Viewer (IGV) 

Visualization of large heterogeneous datasets, providing a smooth and intuitive user experience at all levels of genome resolution

https://www.broadinstitute.org/igv/

ZOOM Lite 

NGS data mapping and visualization software

http://bioinfor.com/zoom/lite/

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/35883/arcs-scaffolding-genome-drafts-with-linked-reads Tue, 06 Mar 2018 16:35:26 -0600 https://bioinformaticsonline.com/bookmarks/view/35883/arcs-scaffolding-genome-drafts-with-linked-reads <![CDATA[ARCS: scaffolding genome drafts with linked reads]]> ARCS, an application that utilizes the barcoding information contained in linked reads to further organize draft genomes into highly contiguous assemblies. We show how the contiguity of an ABySS H.sapiensgenome assembly can be increased over six-fold, using moderate coverage (25-fold) Chromium data. We expect ARCS to have broad utility in harnessing the barcoding information contained in linked read data for connecting high-quality sequences in genome assembly drafts.

Address of the bookmark: https://github.com/bcgsc/ARCS/

]]> Rahul Nayak