Bio-Express

Homer_makeUCSC

The program works by approximating the ChIP-fragment density at each position in the genome. This is done by starting with each tag and extending it by the estimated fragment length (determined by autocorrelation, or it can be manually specified using "-fragLength <#>"). The ChIP-fragment density is then defined as the total number of overlapping fragments at each position in the genome. Below is a diagram that depicts how this works:

IDBX03-20240507-0728
Root CategoryBioinformatics
Sub CategoryEpigenome-Sequencing
EnvironmentBX_ENV_00
LanguageBash
CoreSingle
Version4.9.1
Registrantbioex
Registed Date2024-05-07
Modified Date2024-05-07

#Homer#chip-seq

http://homer.ucsd.edu/homer/ngs/annotation.html

Macs2

With the improvement of sequencing techniques, chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) is getting popular to study genome-wide protein-DNA interactions. To address the lack of powerful ChIP-Seq analysis method, we presented the Model-based Analysis of ChIP-Seq (MACS), for identifying transcript factor binding sites. MACS captures the influence of genome complexity to evaluate the significance of enriched ChIP regions and MACS improves the spatial resolution of binding sites through combining the information of both sequencing tag position and orientation. MACS can be easily used for ChIP-Seq data alone, or with a control sample with the increase of specificity. Moreover, as a general peak-caller, MACS can also be applied to any "DNA enrichment assays" if the question to be asked is simply: where we can find significant reads coverage than the random background.

IDBX03-20240507-0730
Root CategoryBioinformatics
Sub CategoryEpigenome-Sequencing
EnvironmentBX_ENV_00
LanguageBash
CoreSingle
Version2.2.7.1
Registrantbioex
Registed Date2024-05-07
Modified Date2024-05-07

#Macs2#epigenome

https://pypi.org/project/MACS2/

Homer_annotatePeaks

Homer contains a useful, all-in-one program for performing peak annotation called annotatePeaks.pl. In addition to associating peaks with nearby genes, annotatePeaks.pl can perform Gene Ontology Analysis, genomic feature association analysis (Genome Ontology), associate peaks with gene expression data, calculate ChIP-Seq Tag densities from different experiments, and find motif occurrences in peaks. annotatePeaks.pl can also be used to create histograms and heatmaps. Description of the annotation functions are covered below, while quantification of tags, motifs, histograms, etc. are covered here.

IDBX03-20240507-0729
Root CategoryBioinformatics
Sub CategoryEpigenome-Sequencing
EnvironmentBX_ENV_00
LanguageBash
CoreSingle
Version4.9.1
Registrantbioex
Registed Date2024-05-07
Modified Date2024-05-07

#Homer#annoation#genomic regions

http://homer.ucsd.edu/homer/ngs/annotation.html

Bowtie2

Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s of characters to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with an FM Index (based on the Burrows-Wheeler Transform or BWT) to keep its memory footprint small: for the human genome, its memory footprint is typically around 3.2 gigabytes of RAM. Bowtie 2 supports gapped, local, and paired-end alignment modes. Multiple processors can be used simultaneously to achieve greater alignment speed. Bowtie 2 outputs alignments in SAM format, enabling interoperation with a large number of other tools (e.g. SAMtools, GATK) that use SAM. Bowtie 2 is distributed under the GPLv3 license, and it runs on the command line under Windows, Mac OS X and Linux and BSD. Bowtie 2 is often the first step in pipelines for comparative genomics, including for variation calling, ChIP-seq, RNA-seq, BS-seq. Bowtie 2 and Bowtie (also called "Bowtie 1" here) are also tightly integrated into many other tools, some of which are listed here.

IDBX03-20240506-0726
Root CategoryBioinformatics
Sub CategoryEpigenome-Sequencing
EnvironmentBX_ENV_00
LanguageBash
CoreMulti
Version2.4.5
Registrantbioex
Registed Date2024-05-06
Modified Date2024-05-06

#Bowtie2#epigenome

https://bowtie-bio.sourceforge.net/bowtie2/index.shtml

Fastq_qaulity_filter

The FASTX-Toolkit is a collection of command line tools for Short-Reads FASTA/FASTQ files preprocessing. FASTQ-Quality-Filter - removes low-quality sequences from FASTQ files.

IDBX03-20240506-0725
Root CategoryBioinformatics
Sub CategoryEpigenome-Sequencing
EnvironmentBX_ENV_00
LanguageBash
CoreSingle
Version0.0.13.2
Registrantbioex
Registed Date2024-05-06
Modified Date2024-05-06

#fastx-toolkit#fastq_quality_filter#epogenime

http://hannonlab.cshl.edu/fastx_toolkit/commandline.html#fastq_quality_filter_usage

RunEmbed

Execute embedding algorithms. FIt-SNE, T-SNE, UMAP. T-SNE is T-distributed Stochastic Neighbor Embedding method and is a tool to visualize high-dimensional data. FIt-SNE is FFT-accelerated Interpolation-based t-SNE. It is more faster than original t-SNE. Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction.

IDBX03-20240221-0709
Root CategoryBioinformatics
Sub CategorySingle-Cell-RNA-Sequencing
EnvironmentBX_ENV_00
LanguagePython
CoreSingle
Version0.3
Registrantbioex
Registed Date2024-02-21
Modified Date2024-02-21

#fit-sne#tsne#umap#embedding

https://github.com/KlugerLab/FIt-SNE

Demultiplexing

This QIIME 2 plugin supports demultiplexing of single-end and paired-end sequence reads and visualization of sequence quality information.

IDBX03-20231017-0669
Root CategoryTools
Sub CategoryQiime2
EnvironmentBX_ENV_00
LanguagePython
CoreMulti
Version1.0
Registrantcogate
Registed Date2023-10-17
Modified Date2023-10-17

#Demultiplexing

https://docs.qiime2.org/2023.9/plugins/available/demux/

OTU_clustering

IDBX03-20231017-0674
Root CategoryTools
Sub CategoryQiime2
EnvironmentBX_ENV_00
LanguagePython
CoreMulti
Version1.0
Registrantcogate
Registed Date2023-10-17
Modified Date2023-10-17

#OTU_clustering

https://docs.qiime2.org/2023.9/tutorials/qiime2-for-experienced-microbiome-researchers/#otu-clustering

Taxonomy_Assigning

Assigning taxonomy to ASV or OTU representative sequences.

IDBX03-20231017-0676
Root CategoryTools
Sub CategoryQiime2
EnvironmentBX_ENV_00
LanguagePython
CoreMulti
Version1.0
Registrantcogate
Registed Date2023-10-17
Modified Date2023-10-17

#Taxonomy

https://docs.qiime2.org/2023.9/tutorials/qiime2-for-experienced-microbiome-researchers/#id13

Sequence_Feature

Sequence Feature

IDBX03-20231017-0673
Root CategoryTools
Sub CategoryQiime2
EnvironmentBX_ENV_00
LanguagePython
CoreMulti
Version1.0
Registrantcogate
Registed Date2023-10-17
Modified Date2023-10-24

#Sequence_Feature

https://docs.qiime2.org/2023.9/tutorials/qiime2-for-experienced-microbiome-researchers/#grouping-similar-sequences

INPUT
Name	Required	Value	Type
data area 01	data area 01 data area 01data area 01data area 01data area 01data area 01	data area 01data area 01data area 01data area 01data area 01data area 01data area 01	data area 01

OUTPUT
Name	Required	Value	Type
content area 01	content area 01content area 01content area 01	content area 01	content area 01
content area 02	content area 02	content area 02	content area 02

OPTION
Name	Required	Value	Type
content area 01	content area 01	content area 01	content area 01
content area 03	content area 03	content area 03	content area 03
content area 04	content area 04	content area 04	content area 04

Public Contents

Analysis Program

Public Contents

Analysis Program

title