Address of the bookmark: https://cmdcolin.github.io/awesome-genome-visualization/?latest=true&selected=%23BRIG&tag=Comparative

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
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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$

Research Area:
Analysis of Next Generation sequence data in cancer
Methods for analysis of structural variation in cancer genomes
Next Generation sequencing in malaria
Computational comparative genomics
Sensitive genomic sequence search techniques using hidden Markov models
Tasmanian devil facial tumour disease

Link @ http://www.wehi.edu.au/faculty_members/dr_tony_papenfuss

The importance of transcriptional control has been explored in a burgeoning line of research over several decades; nevertheless, we are still far from having a complete picture of the regulatory mechanisms of genes and non-coding RNAs, and their influences on different phenotypes and disease states of a cell. Recent shifts towards large-scale analyses of transcriptional regulation on a sequence and epigenetic level are at the forefront of research, mainly due to sequencing technology advancements and a deeper understanding of the fundamental regulatory processes involved.

Arriving at a better understanding of the influence of specific parts of the overall regulatory machinery on disease states is a high priority of the group’s research agenda.

We are seeking an enthusiastic student to join the group as a PhD student. Applicants must have a BSc(Hons) or MSc degree in a relevant discipline and a willingness to learn and apply new techniques and work in a team. Both local and international students are encouraged to apply.

The studentship covers all university fees and an annual tax-exempt stipend of NZ$22,000 for three years.

Sebastian Schmeier recently joined Massey University and started his own research group in Auckland, New Zealand, a city regularly ranked one of the most livable in the world. This is your chance to experience the amazing Auckland lifestyle and the excitement of joining a young new science team, while staying connected to world class scientific networks.

To apply for the post, please send a cover letter stating your interest in the position and why you think you would be a good candidate, a Curriculum Vitae, a copy of your academic transcript, a sample of your written scientific work, and the names of three referees. Applications will be accepted until the position is filled.

Enquiries and applications to Sebastian Schmeier (s.schmeier@massey.ac.nz).

The evolutionary connections between organisms are represented graphically through phylogenetic trees. Due to the fact that evolution takes place over long periods of time that cannot be observed directly, biologists must reconstruct phylogenies by inferring the evolutionary relationships among present-day organisms. 
Application of the techniques that make this possible can be seen in the very limited field of human genetics, such as the ever more popular use of genetic testing to determine a child's paternity, as well as the emergence of a new branch of criminal forensics focused on genetic evidence.
The effect on traditional scientific classification schemes in the biological sciences has been dramatic as well. Work that was once immensely labor- and materials-intensive can now be done quickly and easily, leading to yet another source of information becoming available for systematic and taxonomic appraisal. This particular kind of data has become so popular that taxonomical schemes based solely on molecular data may be encountered. Proponents even claim that taxonomy was previously based on morphology alone, which of course is utter fable.

For additional information on phylogenetics, see list of Phylogenetics Resources on the Internet.

Phylogeny and Reconstructing Phylogenetic Trees: http://aleph0.clarku.edu/~djoyce/java/Phyltree/cover.html
the CBRG and Department of Statistics Phylogeny tutorial: http://www.compbio.ox.ac.uk/tutorials/phylogeny/
TUTORIAL: PHYLOGENETIC ANALYSIS USING PARSIMONY:http://home.cc.umanitoba.ca/~psgendb/GDE/phylogeny/parsimony/phylip.parsimony.html

PHYLIP: http://www.umanitoba.ca/afs/plant_science/psgendb/doc/Phylip/main.html
An Introduction to Molecular Phylogeny: http://bibiserv.techfak.uni-bielefeld.de/gcb04/tutorials/hoef-emden/GCB04Tut.pdf

How to make a phylogenetic tree: http://www.hiv.lanl.gov/content/sequence/TUTORIALS/TREE_TUTORIAL/Treetutorial.html
Phylogenetic Trees: http://cnx.org/content/m11052/latest/
Phylogeny by Ron Shamir: http://www.cs.tau.ac.il/~rshamir/algmb/01/scribe08/lec08.pdf
Introduction to Phylogeny: http://www.utm.edu/departments/cens/biology/rirwin/391/391Phylog.htm
Lecturer notes on Phylogeny: http://www.sbc.su.se/~bens/course_material/phylocourse1/lecture2.pdf
Principles and Practice of Phylogenetic Systematics:http://www.faculty.biol.ttu.edu/Strauss/Phylogenetics/LectureNotes.htm

Inferring phylogenetic trees: http://www.cis.hut.fi/Opinnot/T-61.6070/slides2008/pres_6070.pdf

Lecture Notes

Chapter 1 - The Diversity, Classification, and Evolution of Vertebrates:http://academic.emporia.edu/mooredwi/nathist/chap1.htm

Algorithms for Phylogenetic Reconstructions:http://lectures.molgen.mpg.de/Algorithmische_Bioinformatik_WS0405/phylogeny_script.pdf

Phylogeny.fr is a free, simple to use web service dedicated to reconstructing and analysing phylogenetic relationships between molecular sequences. Phylogeny.fr runs and connects various bioinformatics programs to reconstruct a robust phylogenetic tree from a set of sequences. For more detail : http://www.phylogeny.fr/version2_cgi/index.cgi

A Brief Tutorial on Phylogenetics
http://bioss.ac.uk/~dirk/talks/tutorial_phylogenetics.pdf

A Brief Tutorial on Phylogenetics Human Rabbit Chicken
http://bioss.ac.uk/~dirk/talks/psnup_tutorial_phylogenetics.pdf

Phylogenetic Tree Computation Tutorial Overview
http://pga.lbl.gov/Workshop/April2002/lectures/Olken.pdf

MrBayes: A program for the Bayesian inference of phylogeny
http://golab.unl.edu/teaching/SBseminar/manual.pdf

Web sites providing software for the construction of phylogenetic trees

• BioEdit

• Coelocanth-Fish Out of Time

• Computational Biochemistry Research Group

• Digital Taxonomy

• Hennig 86

• Hyperclean from Bioinformatics Solutions, Inc.

• Memorial University of Newfoundland

• Mr. Bayes

• NONA

• Oxford University Evolutionary Biology Group

• Paleobiology Database

• PAUP

• Phylip Homepage

• Poy

• Sinauer Associates

• TNT-Tree Analysis Using New Technology

• Tree Base

• Treefinder

• Tree-Puzzle

• Tree View-Taxonomy and Systematics Group at Glasgow

• Washington University-List of Phylogeny Software

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http://www.interpretomics.co/

The genomics platform uses cancer genome sequencing and other high-throughput techniques to identify genes critical to the development of cancer and anomalies in the genomic profile of the tumours.

For more info visit : http://oicr.on.ca/

Next generation quantitative genetics in plants. Jiménez-Gómez, Frontiers in Plant Science 2:77, 2011 Full Text [equally relevant to animal and microbial systems]

Sense from sequence reads: methods for alignment and assembly. Flicek & Birney, Nat Methods 6(11 Suppl):S6-S12, 2009. Full Text

Library construction and experimental design

Statistical design and analysis of RNA sequencing data. Auer & Doerge, Genetics 185(2):405-16, 2010. PubMedCentral

Biases in Illumina transcriptome sequencing caused by random hexamer priming. Hansen et al., Nucleic Acids Res. 38(12): e131, 2010. PubMedCentral

Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Aird et al, Genome Biology 12:R18, 2011 Full Text

Amplification-free Illumina sequencing-library preparation facilitates improved mapping and assembly of GC-biased genomes. Kozarewa et al, Nature Methods 6(4):291-5, 2009 PubMedCentral

Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Rohland & Reich, Genome Research 22(5): 939–946. PubMedCentral

Data formats, data management, and alignment software tools

The Sequence Alignment/Map format and SAMtools. Li et al, Bioinformatics 25(16):2078-9, 2009 PubMedCentral

SAM format specification file

Efficient storage of high throughput sequencing data using reference-based compression. Fritz et al, Genome Res 21(5):734-40, 2011. Full Text

Compression of DNA sequence reads in FASTQ format. Deorowicz & Grabowski, Bioinformatics 27(6):860-2, 2011. PubMed

Fast and accurate short read alignment with Burrows-Wheeler transform. Li & Durbin, Bioinformatics 25(14):1754-60, 2009. PubMedCentral

Improving SNP discovery by base alignment quality. Li H, Bioinformatics 27(8):1157-8, 2011. PubMed

BEDTools: a flexible suite of utilities for comparing genomic features. Quinlan and Hall, Bioinformatics 26:841-842, 2010. Publisher Website

Data quality assessment, filtering, and correction

SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. Cox et al, BMC Bioinformatics 11:485, 2010. PubMedCentral

TileQC: a system for tile-based quality control of Solexa data. Dolan & Denver, BMC Bioinformatics 9:250, 2008 PubMedCentral [requires a reference sequence]

Quake: quality-aware detection and correction of sequencing errors. Kelley et al, Genome Biol 11(11):R116, 2010. PubMed

FastQC: a quality control tool for high-throughput sequence data. Home Page

FASTX-toolkit: FASTQ/A short-reads pre-processing tools Home Page

Reference-free validation of short read data. Schröder et al, PLoS One 5(9):e12681, 2010. PubMedCentral

Correction of sequencing errors in a mixed set of reads. Salmela, Bioinformatics 26(10):1284, 2010. Full Text [includes error correction of SOLiD reads in colorspace]

Repeat-aware modeling and correction of short read errors. Yang et al, BMC Bioinformatics 12(Supp1):S52, 2011 PubMedCentral [requires a reference sequence]

HiTEC: accurate error correction in high-throughput sequencing data. Ilie et al, Bioinformatics 27(3):295, 2011 Full Text

Error correction of high-throughput sequencing datasets with non-uniform coverage. Medvedev et al., Bioinformatics 27(13):i137-41, 2011. PubMedCentral

De novo assembly

Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Zerbino & Birney, Genome Res 18(5):821-9, 2008. u>PubMedCentral

Assembly of large genomes using second-generation sequencing. Schatz et al, Genome Res 20(9):1165-73, 2010. PubMedCentral

High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Gnerre et al, PNAS 108(4): 1513-18, 2011 PubMedCentral

Genome assembly has a major impact on gene content: a comparison of annotation in two Bos taurus assemblies. Florea  et al., PLoS One 6(6):e21400, 2011. PubMedCentral

Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Carver et al, Bioinformatics 28(4):464 - 469, 2012 PubMedCentral

Efficient de novo assembly of large genomes using compressed data structures. Simpson & Durbin, Genome Research 22:549-556, 2012 Full Text [Describes the String Graph Assembler (SGA), which assembled a human genome in less than 6 days using 54 Gb of RAM and a 123-processor compute cluster for calculation of an FM-index of the 1.2 billion reads]

Readjoiner: a fast and memory efficient string graph-based sequence assembler. Gonnella & Kurtz, BMC Bioinformatics 13: 82, 2012 PubMedCentral

Assemblathon 1: A competitive assessment of de novo short read assembly methods. Earl et al, Genome Research 21:2224-2241, 2011 Full Text

Chromatin immunoprecipation analysis: ChIP-seq

ChIP-seq: advantages and challenges of a maturing technology. Park, Nat Rev Genet. 10:669-80, 2009 PubMed

ChIP-seq and Beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Furey, Nat Rev Genet 13: 840–852, 2012 Publisher Web Site

MuMoD: a Bayesian approach to detect multiple modes of protein–DNA binding from genome-wide ChIP data. Narlikar, Nucleic Acids Res 41:21–32, 2013 PubMed

Transcriptome analysis

Assembly and comparison to genome

Full-length transcriptome assembly from RNA-Seq data without a reference genome. Grabherr et al, Nature Biotechnology 29:644 - 652, 2011. PubMed [The software is called Trinity, and is available on Sourceforge.]

Comprehensive analysis of RNA-Seq data reveals extensive RNA editing in a human transcriptome. Peng et al, Nature Biotechnology 30:253 - 260, 2012. PubMed [Several comments on this paper question whether the reported differences are in fact evidence of editing or are simply sequencing errors - the authors stand by their conclusions, but the controversy demonstrates the importance of robust data analysis methods.]

Optimization of de novo transcriptome assembly from next-generation sequencing data. Surget-Groba & Montoya-Burgos, Genome Res 20(10):1432-40, 2010. Full Text

Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads. Martin et al, BMC Genomics 11:663, 2010 Full Text

De novo assembly and analysis of RNA-seq data. Robertson et al, Nature Methods 7:909-912, 2010 Full Text [describes Trans-ABySS, a pipeline to use the ABySS parallel assembler for de novo transcriptome analysis]

Differential expression analysis

R-SAP: a multi-threading computational pipeline for the characterization of high-throughput RNA-sequencing data. Mittal & McDonald, Nucleic Acids Res, 2012 Full Text

Targeted RNA sequencing reveals the deep complexity of the human transcriptome. Mercer et al, Nature Biotechnology 30:99 - 104, 2012 Publisher Website

Differential gene and transcript expression analysis of RNA-Seq experiments with TopHat and Cufflinks. Trapnell et al, Nature Protocols 7:562 - 578, 2012 Publisher Website

Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Łabaj et al, Bioinformatics 27:i383 - i391, 2011 Full Text

Improving RNA-Seq expression estimates by correcting for fragment bias. Roberts et al, Genome Biol 12:R22, 2011 PubMed Central

Cloud-scale RNA-sequencing differential expression analysis with Myrna. Langmead et al, Genome Biol 11:R83, 2010 Full Text

From RNA-seq reads to differential expression results. Oshlack et al, Genome Biol 11(12):220, 2010 Full Text

DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Wang et al., Bioinformatics. 26(1):136-8. 2010 PubMed

DEseq: Differential expression analysis for sequence count data. Anders and Huber, Genome Biology 11:R106, 2010 Full Text

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Robinson et al., Bioinformatics 26(1):139-40 2010 PubMedCentral

Two-stage Poisson model for testing RNA-seq data. Auer and Doerge, SAGMB 10(1), article 26 Full Text

Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments. McCormick et al., Silence2(1):2, 2011 PubMedCentral

RNA-Seq gene expression estimation with read mapping uncertainty. Li et al, Bioinformatics 26:493-500, 2010 PubMedCentral [describes the RSEM software package]

Comparing genomes and assemblies; variant detection

Versatile and open software for comparing large genomes. Kurtz et al, Genome Biol (5(2):R12, 2004. PubMedCentral [describes the MUMmer software for full-genome alignment & comparisons]

Searching for SNPs with cloud computing. Langmead et al, Genome Biol 10(11):R134, 2009 Full Text

Calling SNPs without a reference sequence. Ratan et al, BMC Bioinformatics 11:130, 2010 PubMedCentral

Microindel detection in short-read sequence data. Krawitz et al, Bioinformatics 26(6):722-9, 2010. Full Text

vipR: variant identification in pooled DNA using R. Altmann et al., Bioinformatics 27: i77-i84, 2011. PubMedCentral

Geoseq: a tool for dissecting deep-sequencing datasets. Gurtowski et al, BMC Bioinformatics 11:506, 2010. PubMedCentral [Geoseq is a web service that allows searching deep sequencing datasets with a reference sequence of a gene of interest]

Detecting and annotating genetic variations using the HugeSeq pipeline. Lam et al, Nature Biotechnology 30:226 - 229, 2012 Publisher Website, Home Page

Genome-wide LORE1 retrotransposon mutagenesis and high-throughput insertion detection in Lotus japonicus. Urbański et al, Plant J 64:731-741, 2012. Publisher Website [This paper describes a 2-dimensional pooling strategy with barcoding to allow use of Illumina sequencing to screen for retrotransposon insertion mutations, and includes a software package called FSTpoolit for analysis of the resulting sequence reads.]

Genotyping by sequencing

Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Davey et al., Nat Rev Genet 12(7):499-510, 2011 PubMed [A review of methods available at the time]

A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. Elshire et al., PLoS One 6(5):e19379, 2011. Full Text

Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. Poland et al., PLoS One 7(2): e32253, 2012. Full Text

Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. Peterson et al, PLoS One 7(5):e37135, . 2012. Full Text

Imputation of unordered markers and the impact on genomic selection accuracy. Rutkowski et al, G3 3(3):427-39, 2013. Full Text

Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high-throughput, highly informative genotyping for molecular breeding of Eucalyptus. Sansaloni et al., BMC Proceedings 5(Suppl 7):P54, 2011 Full Text

High-throughput genotyping by whole-genome resequencing. Huang et al., Genome Res 19(6):1068-76, 2009. Full Text

Multiplexed shotgun genotyping for rapid and efficient genetic mapping. Andolfatto et al. Genome Res 21(4):610-7, 2011. Full Text

Restriction-site Associated DNA (RAD) markers

Rapid SNP discovery and genetic mapping using sequenced RAD markers. Baird et al, PLoS One 3(10):e3376, 2008 Full Text

Linkage mapping and comparative genomics using next-generation RAD sequencing of a non-model organism. Baxter et al., PLoS One 6(4):e19315, 2011. Full Text

Genome evolution and meiotic maps by massively parallel DNA sequencing: spotted gar, an outgroup for the teleost genome duplication. Amores et al, Genetics 188(4):799-808, 2011. PubMed

Construction and application for QTL analysis of a Restriction-site Associated DNA (RAD) linkage map in barley. Chutimanitsakun et al, BMC Genomics 4; 12:4, 2011. Full Text

RAD tag sequencing as a source of SNP markers in Cynara cardunculus L. Scaglione et al., BMC Genomics 13:3, 2012. Full Text

Paired-end RAD-seq for de novo assembly and marker design without available reference. Willing et al., Bioinformatics 27(16):2187-93, 2011. Publisher Website

Local de novo assembly of RAD paired-end contigs using short sequencing reads. Etter et al., PLOS ONE 6(4): e18561, 2011. Full Text

Stacks: building and genotyping loci de novo from short-read sequences. Catchen et al., G3: Genes, Genomes, Genetics, 1:171-182, 2011. Full Text, Home Page

Rainbow: an integrated tool for efficient clustering and assembling RAD-seq reads. Chong et al, Bioinformatics 28(21):2732-7, 2012. Publisher Website

UK RAD Sequencing Wiki page, with bibliography and RADTools software download Home Page

Workspace environments

Papers

Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Goecks et al, Genome Biol 11(8):R86, 2010 PubMedCentral

Galaxy Cloudman: Delivering compute clusters. BMC Bioinformatics 11(Suppl. 12):S4, 2010 Full Text

The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. McKenna et al, Genome Res 20(9):1297-303, 2010. PubMedCentral

A framework for variation discovery and genotyping using next-generation DNA sequencing data. DePristo et al., Nat Genet 43(5):491-8, 2011. PubMed

Online resources

The R statistical computing environment includes Bioconductor, a specialized set of tools for analysis of microarray and high-throughput sequencing data. Introductory materials from on-line or short workshops are widely available online; examples are Evomics2012 Bioconductor-tutorial.pdf, and Intro to Bioconductor. Materials from an advanced course on high-throughput genetic data analysis are at Seattle 2012 materials. Thomas Girke of UC-Riverside has written a very complete set of manuals describing the use of R and Bioconductor for analysis of genomic datasets, available at R and Bioconductor Manuals.
Manuals and contributed documentation for R are available at the R-project.org website, and video tutorials are also available on Youtube; those posted by Tutorlol are brief, clear, and to the point.
Materials from a series of mini-courses in R taught in 2010 at UCLA are available:

• Intro to programming and graphics
• Data manipulation and functions
• Graphics for exploratory data analysis
• Introductory statistics
• Linear regression

A Little Book of R for Bioinformatics is an on-line resource with information and exercises to provide practice in bioinformatics analysis of DNA sequences and other biological data in R.
Many books on specific topics in R programming are also available through Amazon or other vendors.

Cloud computing resources

The case for cloud computing in genome informatics. Lincoln Stein, Genome Biol. 11(5):207, 2010 Pubmed

Galaxy Cloudman: delivering cloud compute clusters. Afgan et al, BMC Bioinformatics 11(Suppl 12):S4, 2010 Full Text

CloudBioLinux is an open-source project that provides a bioinformatics Linux system for cloud computing, pre-configured with a variety of software tools installed and ready to use.

A tutorial on getting started with CloudBioLinux on the Amazon Web Services Elastic Compute Cloud (EC2)

“Deploying Galaxy on the Cloud” – slides from a presentation by Enis Afgan (Emory University) at the
 Bioinformatics Open Source Conference in Boston, July 2010

A screencast that provides a step-by-step guide to starting a Galaxy cluster in the EC2 environment

A webpage that has the same information in text form, and is the basis for the screencast

The iPlant Collaborative, an NSF-funded project to create computational resources for plant biology research, provides access to cloud computing resources through Atmosphere

SeqWare Query Engine: storing and searching sequence data in the cloud. O’Connor et al, BMC Bioinformatics 11(Suppl 12):S2, 2010 Full Text

An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics. Taylor, BMC Bioinformatics 11(Suppl 12):S1, 2010 Full Text

Links to Linux command-line tutorials and resources

Tutorials for AWK, a powerful tool for handling data tables

• A set of awk notes from Boston University
• Bruce Barnett's awk tutorial
• Greg Goebel's awk tutorial
• Executing an awk command from R to simplify data exploratory analysis, from Lex Nederbragt

Tutorials for bash shell scripting

• A tutorial at linuxconfig.org
• A Getting Started With Bash tutorial at hypexr.org
• Mendel Cooper's Advanced Bash Shell-Scripting Guide

Tutorials for sed, the command-line stream editor

• A tutorial at Rutgers
• Peteris Krumins claims to have the World's Best Introduction to Sed; take a look and judge for yourself.
• Bruce Barnett's sed tutorial.

Links to other useful sites

The SEQanswers online community has forums on several topics related to sequencing; the bioinformatics forum is the most active.

The SEQanswers Software Wiki is a list of software for analysis of sequencing data

Biostar is another online community for questions and answers on bioinformatics and computational genomics.

Information on file formats used by the University of California - Santa Cruz Genome Browser is on the FAQ list

A manual for the Integrated Genome Browser visualization tool is here

Course materials for a short course entitled Introduction to R and Bioconductor, held in Seattle in Dec 2010

Genomic Regions Enrichment of Annotations Tool - A web service to test for over-representation of specific ontology categories among genes near ChIP-seq peaks

Next-gen-seq software - a list of software packages, both commercial and open-source, related to analysis of deep sequencing datasets

Software from the Center for Bioinformatics and Computational Biology, University of Maryland - many useful programs, all open-source

PLAZA: a comparative genomics resource to study gene and genome evolution in plants; described by Proost et al, Plant Cell 21:3718, 2010 Full Text

The European Bioinformatics Institute provides tools ArrayExpressHTS and R-Cloud for analysis of transcriptome data