| Clinical and Translational Science Institute

Bioinformatics Data Analysis Pipeline- Spatial Transcriptomics

Space Ranger

Space Ranger is a set of analysis pipelines that process 10x Genomics Visium data with brightfield or fluorescence microscope images, allowing users to map the whole transcriptome in a variety of tissues. Space Ranger v3.0 now supports Visium HD.
Reference: PMID: 28091601
Contact: Qin Ma
BayesSpace

A fully Bayesian statistical method that uses the information from spatial neighborhoods for resolution enhancement of spatial transcriptomic data and for clustering analysis.
Reference: PMID: 34083791
Contact: Qin Ma
SPRUCE

A Bayesian spatial multivariate finite mixture model based on multivariate skew-normal distributions, which is capable of identifying distinct cellular sub-populations in HST data.
Reference: PMID: 35895854
Contact: Qin Ma
SpaGCN

A graph convolutional network approach that integrates gene expression, spatial location and histology in SRT data analysis. Through graph convolution, SpaGCN aggregates gene expression of each spot from its neighboring spots, which enables the identification of spatial domains with coherent expression and histology.
Reference: PMID: 34711970
Contact: Qin Ma
SPARK-X

A non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies.
Reference: PMID: 34154649
Contact: Qin Ma
RCTD

A computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures in spatial transcriptomic while correcting for differences across sequencing technologies.
Reference: PMID: 33603203
Contact: Qin Ma
CARD

A deconvolution method that combines cell-type-specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition across tissue locations.
Reference: PMID: 35501392
Contact: Qin Ma
SpaTalk

A graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data.
Reference: PMID: 35908020
Contact: Qin Ma
COMMOT

It can infer cell–cell communication in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells.
Reference: PMID: 36690742
Contact: Qin Ma

Bioinformatics Data Analysis Pipeline- Single Cell RNA-Seq

Cell Ranger

A set of analysis pipelines that perform sample demultiplexing, barcode processing, single cell 3' and 5' gene counting, V(D)J transcript sequence assembly and annotation and Feature Barcode analysis from single cell data.
Reference: PMID: 28091601
Contact: Qin Ma
Seurat

Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. Seurat aims to enable users to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements and to integrate diverse types of single-cell data.
Reference: PMID: 37231261
Contact: Qin Ma
Monocole3

An unsupervised algorithm to infer transcriptome dynamics of a temporal process such as cell differentiation using single-cell RNA-Seq data.
Reference: PMID: 24658644
Contact: Qin Ma
Slingshot

A method for inferring cell lineages and pseudotimes from single-cell gene expression data.
Reference: PMID: 29914354
Contact: Qin Ma
SingleR

A computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes.
Reference: PMID: 30643263
Contact: Qin Ma
ScType

A fully-automated and ultra-fast cell-type identification based solely on a given scRNA-seq data, along with a comprehensive cell marker database as background information.
Reference: PMID: 35273156
Contact: Qin Ma
Azimuth

A web application that uses an annotated reference dataset to automate the processing, analysis and interpretation of a new single-cell RNA-seq or ATAC-seq experiment.
Reference: PMID: 31178118
Contact: Qin Ma
Scanpy

A scalable toolkit for analyzing single-cell gene expression data. It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing.
Reference: PMID: 29409532
Contact: Qin Ma
Harmony

An algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions.
Reference: PMID: 31740819
Contact: Qin Ma
MAGIC

A method that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts.
Reference: PMID: 29961576
Contact: Qin Ma
CellChat

A tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data
Reference: PMID: 33597522
Contact: Qin Ma
IRIS3

A web server for active regulons within a specific cell type, i.e., cell-type-specific regulons (CTSR), inference from scRNA-Seq data for human and mouse
Reference: PMID: 32421805
Contact: Qin Ma
ScGNN

scGNN (single-cell graph neural network) can provide a hypothesis-free deep learning framework for scRNA-Seq analyses. scGNN provides an effective representation of gene expression and cell–cell relationships. It is also a powerful framework that can be applied to general scRNA-Seq analyses.
Reference: PMID: 33767197
Contact: Qin Ma

Bioinformatics Data Analysis Pipeline- TCR-Seq

ClusTCR

TCR-Seq
A Python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity.
Reference: https://doi.org/10.1093/bioinformatics/btab446
Contact: Qin Ma
TCRosetta

TCR-Seq
An integrated analysis and annotation platform for T-cell receptor sequences.
Reference: https://doi.org/10.1093/gpbjnl/qzae013
Contact: Qin Ma
Immunarch

TCR-Seq
Fast and Seamless Exploration of Single-cell and Bulk T-cell/Antibody Immune Repertoires in R.
Reference: https://github.com/immunomind/immunarch
Contact: Qin Ma
scRepertoire

TCR-Seq
An R-based toolkit for single-cell immune receptor analysis.
Reference: PMID: 32789006
Contact: Qin Ma

Bioinformatics Data Analysis Pipeline- Genetic Variant Analysis

GATK

Genome Analysis Toolkit Best Practices Pipeline
Reference: PMID: 25431634
Contact: Qin Ma
VarScan

Tool for detecting SNPs,indels, somatic mutations and copy number variants in exome data.
Reference: PMID: 22300766
Contact: Qin Ma
HaplotypeCaller

Tool under GATK to call germline SNPs and indels via local re-assembly of haplotypes.
Reference: https://www.biorxiv.org/content/10.1101/201178v3
Contact: Qin Ma
Mutect

Tool under GATK to call somatic SNVs and indels via local assembly of haplotypes.
Reference: PMID: 20644199
Contact: Qin Ma
CNVKit

Genome wide copy number detection and visualization from targeted DNA sequencing.
Reference: PMID: 27100738
Contact: Qin Ma
VEP

Variant Effect Predictor to annotate variants using Ensembl.
Reference: PMID: 27268795
Contact: Qin Ma
VCFtools

A package designed to access methods to manage complex genetic variation in form of vcf format.
Reference: PMID: 21653522
Contact: Qin Ma
Picard

A set of command line tools for manipulating high-throughput sequencing (HTS) data and formats such as SAM/BAM/CRAM and VCF.
Reference: https://broadinstitute.github.io/picard/
Contact: Qin Ma

Bioinformatics Data Analysis Pipeline- Methylation

Bismark

A tool to map bisulfite converted sequence reads and determine cytosine methylation states.
Reference: PMID: 21493656
Contact: Qin Ma
TrimGalore

A wrapper around Cutadapt and FastQC to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data.
Contact: Qin Ma
DSS

An R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq)
Reference: PMID: 23001152
Contact: Qin Ma
MethylKit

R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing.
Reference: PMID: 23034086
Contact: Qin Ma

Bioinformatics Data Analysis Pipeline- ChIP-Seq

DeepTools

Tools for exploring deep sequencing data especially ChIPseq.
Reference: PMID: 27079975
Contact: Qin Ma
MACS2

Transcription factor peak caller for CHIPseq and any other type of DNA enrichment.
Reference: PMID: 18798982
Contact: Qin Ma
ChIPpeakAnno

Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments, or any experiments that result in large number of genomic interval data.
Reference: PMID: 20459804
Contact: Qin Ma
DiffBind

Differential Binding Analysis of ChIP-Seq Peak Data.
Reference: PMID: 25972895
Contact: Qin Ma
HOMER

Software for motif discovery and next-gen sequencing analysis.
Reference: PMID: 20513432
Contact: Qin Ma

Bioinformatics Data Analysis Pipeline- RNA-Seq

EdgeR

Differential expression analysis using bulk RNA-seq data
Reference: PMID: 19910308
Contact: Qin Ma
DESeq2

Differential expression analysis using bulk RNA-seq data.
Reference: PMID: 25516281
Contact: Qin Ma
Limma

Differential expression analysis using bulk RNA-seq and microarray data.
Reference: PMID: 25605792
Contact: Qin Ma
RSeQC

RNAseq quality control package.
Reference: PMID:22743226
Contact: Qin Ma
JunctionSeq

Bioconductor package for detection and visualization of differential usage of exons and splice junctions in High-Throughput, Next-Generation RNA-Seq datasets.
Reference: PMID: 27257077
Contact: Qin Ma
CIBERSORT

A deconvolution-based method thats estimates each immune cell type's relative proportion from a gene expression profile (GEP).
Reference: PMID: 29344893
Contact: Qin Ma

Bioinformatics Data Analysis Pipeline- General Tools

Bowtie2

Ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences,
Reference: PMID: 22388286
Contact: Qin Ma
LEfSe

Determines the features (organisms, clades, operational taxonomic units, genes or functions) most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance.
Reference: PMID: 21702898
Contact: Qin Ma
FastQC

A quality control tool for high throughput sequence data.
Reference: PMID: 21702898
Contact: Qin Ma
Samtools

Reading/writing/editing/indexing/viewing SAM/BAM/CRAM format.
Reference: PMID: 33590861
Contact: Qin Ma
HISAT2

HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes as well as to a single reference genome.
Reference: PMID: 25751142
Contact: Qin Ma
Bwa

Software package for mapping DNA sequences against a large reference genome.
Reference: PMID: 19451168
Contact: Qin Ma
Bedtools

Versatile command line toolkit to analyze genomic features.
Reference: PMID:20110278
Contact: Qin Ma
Featurecounts

An efficient general purpose program for assigning sequence reads to genomic features.
Reference: PMID: 24227677
Contact: Qin Ma
Cutadapt

Tool used for adapter trimming.
Reference: https://doi.org/10.14806/ej.17.1.200
Contact: Qin Ma
Integrative Genomics Viewer

Viewer for sequence alignment data.
Reference: PMID: 21221095
Contact: Qin Ma
Nf-Core

A community effort to collect a curated set of analysis pipelines built using Nextflow.
Reference: PMID: 32055031
Contact: Qin Ma

Bioinformatics Data Analysis Pipeline

These resources are divided by the following categories: General Tools, RNA-Seq, ChIP-Seq, Methylation, Genetic Variant Analysis, TCR-Seq, Single Cell RNA-Seq, Spatial Transcriptomics, Other Bioinformatics Analyses, Pathway Analysis and Omics Experimental Design.

Bioinformatics Data Analysis Pipeline Resources

General Tools

RNA-Seq

ChIP-Seq

Methylation

Genetic Variant Analysis

TCR-Seq

Single Cell RNA-Seq

Spatial Transcriptomics

Pathway Analysis

Omics Experimental Design

Other Bioinformatics Analyses

Generative AI

Ohio State encourages faculty, staff and students to use modern generative AI tools to accelerate scholarship, education and innovation. Use these tools thoughtfully and AI is not approved for all use cases: enter only public (S1) information unless the tool is explicitly institutionally approved for higher-risk data. Some AI capabilities are available through university-supported agreements (e.g., enterprise offerings), and those should be used when appropriate. Users remain responsible for protecting sensitive, restricted or regulated information and for complying with applicable Ohio State and Ohio State Wexner Medical Center policies.