Warden and Wu Preprint: v1

In general, this primarily focuses on the following types of comparisons:

1. Cell line experiments with over-expression or knock-down to define a known causal gene, with processing starting with public reads.
2. Processed TCGA (The Cancer Genome Atlas) data for breast cancer (BRCA) to compare gene expression by immunohistochemistry status (ER/ESR1, PR/PGR, or HER2/ERBB2).

Differential expression methods include the following:

• edgeR (GLM)
• edgeR-robust (GLM)
• edgeR (QL)
• edgeR-robust (QL)
• DESeq1
• DESeq2
• limma-voom
• limma-trend (CPM)
• limma-trend (FPKM/RPKM)
• ANOVA (log2 FRPKM/RPKM)

The most common preprocessing strategies include STAR, TopHat2, and Salmon. However, a limited amount of additional processing with HISAT2, kallisto, Bowtie2 (+eXpress), and Bowtie1 (+RSEM) is also provided.

Most STAR and TopHat2 alignments use htseq-count for quantification, as well as running cuffdiff (for single variable 2-group comparisons). However, a limited amount of additional processing with featureCounts is also provided.

Most STAR and TopHat2 alignments start with the public forward reads, even if paired-end data was available.

EdgeR results from MMGs. Differential expression results calculated by edgeR for MMG counts produced by the stage 2 analysis. Can be downloaded from [43]. (XLSX 428 kb)

EdgeR results from MMGs. Differential expression results calculated by edgeR for MMG counts produced by the stage 2 analysis. Can be downloaded from [43]. (XLSX 428 kb)

EdgeR results from unique counts. Differential expression results calculated by edgeR for gene counts produced by the stage 1 analysis. Can be downloaded from [43]. (XLSX 2159 kb)

Figure S1, Venn diagram showing the number of differentially expressed genes identified by two versions of Cuffdiff2. Figure S2, The effects of biological replicates on the differential expression analysis for Cuffdiff v2.0.2. Figure S3, The detected fold changes of all the differentially expressed genes identified by three tools were compared and shown, including DESeq vs. edgeR (top panel), DESeq vs. Cuffdiff2 (middle panel) and edgeR vs. Cuffdiff2 (bottom panel). File S1, Analysis pipelines, methods and examples of commands for differential expression analysis, subsampling fastq files and generating SAM/BAM files based on simulated count values. File S2, The raw count values for genes with high fold changes were picked up by edgeR but not by DESeq. Genes with high fold changes (the absolute value of log2 fold changes larger than 2) identified as DEGs by edgeR but not by DESeq are listed in the file. The gene ID, the log2 fold changes (logFC) and FDR from DESeq, the logFC and FDR from edgeR, the raw count values for the four replicates of sample K (K1–K4) and sample N (N1–N4) are shown in each of the columns. Table S1, Numbers of reads for the human hbr and uhr samples from the MAQC dataset. Table S2, Numbers of reads for the mouse neurosphere samples for treatment groups of K and N (the K_N dataset). Table S3, The number of reads for each individual sample of the LCL3 dataset. Table S4, The definition for TP, FP, TN, FN, TPR and FPR. Table S5, The false positive rate for Cuffdiff2, DESeq and edgeR based on the LCL1 dataset. (ZIP)

This data set provides data files and R code to accompany the article Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR published by F1000Research.

The data consists of Reduced Representation BS-seq methylation profiles of epithelial populations from the mouse mammary gland, with n=2 biological replicates for each of three cell populations.

RNA-seq expression profiles of luminal and basal mammary epithelial populations are also provided.

The R code undertakes an differential methylation analysis of the BS-seq profiles and demonstrates a strong negative correlation between the differential methylation and differential expression results.

A number of factors influence vaccination effectiveness, including age, sex, and comorbidities. A transcriptome analysis was performed via RNA sequencing. The genes with immunological functions are increased in expression in individuals with high pre-existing immunity. Based on the transcriptome analysis, the set of genes can be used to predict a vaccine response.

Data set 1. Transcript expression across human RNA-Seq samples: estimated read counts. The file contains estimated read counts, generated by kallisto (https://pachterlab.github.io/kallisto/), for human transcripts and RNA-Seq samples used in this study (see Additional file 2 of the accompanying publication). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. Ensembl transcript identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively. Data set 2. Transcript expression across murine RNA-Seq samples: estimated read counts. As in Data set 1, but for mouse transcripts. Data set 3. Transcript expression across simian RNA-Seq samples: estimated read counts. As in Data set 1, but for chimpanzee transcripts. Data set 4. Transcript expression across across human RNA-Seq samples: estimated transcript abundances. As in Data set 1, but instead of read counts, transcript abundances in transcripts per million (TPM), as estimated by kallisto (https://pachterlab.github.io/kallisto/), are listed. Format, column and row names as in Data set 1. Data set 5. Transcript expression across murine RNA-Seq samples: estimated transcript abundances. As in Data set 4, but for mouse transcripts. Data set 6. Transcript expression across simian RNA-Seq samples: estimated transcript abundances. As in Data set 4, but for chimpanzee transcripts. Data set 7. Differential expression analyses across human RNA-Seq sample groups: log fold changes. The file contains log fold changes, inferred by edgeR (http://bioconductor.org/packages/release/bioc/html/edgeR.html), for human genes and the RNA-Seq sample group contrasts listed in Additional file 3 of the accompanying publication in a compressed (GZIP) TSV gene-by-comparison matrix. Ensembl gene identifiers and a descriptive contrast identifier serve as row and column names, respectively. Data set 8. Differential expression analyses across murine RNA-Seq sample groups: log fold changes. As in Data set 7, but for mouse genes. Data set 9. Differential expression analyses across simian RNA-Seq sample groups: log fold changes. As in Data set 7, but for chimpanzee genes. Data set 10. Differential expression analyses across human RNA-Seq sample groups: false discovery rates. The file contains false discovery rates (FDR) for the differential expression analyses summarized in Data set 7. Format, column and row names as in Data set 7. Data set 11. Differential expression analyses across murine RNA-Seq sample groups: false discovery rates. As in Data set 10, but for mouse genes. Data set 12. Differential expression analyses across simian RNA-Seq sample groups: false discovery rates. As in Data set 10, but for chimpanzee genes. Data set 13. Quantification of alternative splicing events across human RNA-Seq samples. The file contains ‘percent spliced in’ (PSI) values computed by SUPPA (https://github.com/comprna/SUPPA) for annotated alternative splicing events (inferred from the transcript annotation of the human genome, Ensembl release 84; http://www.ensembl.org/). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. SUPPA-provided event identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively. Data set 14. Quantification of alternative splicing events across murine RNA-Seq samples. As in Data set 13, but for mouse alternative splicing events. Data set 15. Differential splicing analyses across human RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). The file contains ΔPSI values for human alternative splicing events (as in Data set 13). The RNA-Seq sample group contrasts are listed in Additional file 3 of the accompanying publication. Values were inferred by SUPPA’s diffSplice functionality (https://github.com/comprna/SUPPA). The format is a compressed (GZIP) tab-separated gene-by-comparison matrix. SUPPA event identifiers and a descriptive contrast identifier serve as row and column names, respectively. Data set 16. Differential splicing analyses across murine RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). As in Data set 15, but for mouse alternative splicing events. Data set 17. Differential splicing analyses across human RNA-Seq sample groups: P values. The file contains P values for the differential splicing analysis of human alternative splicing events summarized in Data set 15. Format, column and row names as in Data set 15. Data set 18. Differential splicing analyses across murine RNA-Seq sample groups: P values. The file contains P values for the differential splicing analysis of mouse alternative splicing events summarized in Data set 16. Format, column and row names as in Data set 15. {"references": ["Alamancos, G. P., Pag\u00e8s, A., Trincado, J. L., Bellora, N. & Eyras, E. Leveraging transcript quantification for fast computation of alternative splicing profiles. ...

Correlation coefficients for each method compared to RT-qPCR. (XLSX 10 kb)

Transcriptome Shotgun Sequencing (RNA-seq) has been readily embraced by geneticists and molecular ecologists alike. As with all high-throughput technologies, it is critical to understand which analytic strategies are best suited and which parameters may bias the interpretation of the data. Here we use a comprehensive simulation approach to explore how various features of the transcriptome (complexity, degree of polymorphism π, alternative splicing), technological processing (sequencing error ε, library normalization) and bioinformatic workflow (de novo vs. mapping assembly, reference genome quality) impact transcriptome quality and inference of differential gene expression (DE). We find that transcriptome assembly and gene expression profiling (edgeR vs. baySeq software) works well even in the absence of a reference genome, and is robust across a broad range of parameters. We advise against library normalization, and in most situations advocate mapping assemblies to an annotated genome ...

Conventional (bulk) RNA-sequencing was performed on unfractionated cell suspension or snap frozen whole tissue material. Total RNA was isolated with TRIzol reagent followed by purification over PureLink RNA Mini Kit columns (Invitrogen). RNA-seq was performed using a polyA-enriched strand-specific library construction protocol (doi: 10.1016/j.ccell.2016.02.009) and paired-end 75bp sequencing on an Illumina HiSeq 2500 instrument. Raw reads were aligned to the reference human genome assembly GRCh37 (hg19) using STAR (v2.5.2.a). To improve spliced alignment, STAR was provided with exon junction coordinates from the reference annotations (Gencode v19). We applied a modified version of a bioinformatics workflow for normalization of raw read counts and differential gene expression analysis (doi: 10.12688/f1000research.9005.3). Gene-level read counts were quantified using HTSEQ-count (v0.11.0; intersection-strict, reverse mode) (doi: 10.1093/bioinformatics/btu638). Genes showing low read counts (i.e., genes not showing counts per million (cpm) > 1.0 in at least 10% of samples) were removed from further analysis. Raw counts from expressed genes were then TMM-normalized and scaled to counts per million (CPM) using the edgeR (v3.22.2) package (doi: 10.1093/bioinformatics/btp616). Sample IDs correspond to those referenced in Wang X et al, Nature Communications (2022).

Sample type: SRA

Source name: bone marrow derived Organism: Mus musculus Characteristics
strain: C57BL/6 Sex: male age: 8 to 10 weeks Growth protocol: After 1 day in 0.6ng/ml CSF1 in alpha+ MEM /15% FCS, non-adherent cells were incubated for two days in a fresh dish containing 12ng/ml CSF1 in alpha+ MEM /10% FCS and then for 7 days in 120ng/ml CSF1 in alpha+ MEM /10% FCS. Cells were incubated for a further two days in fresh alpha+ MEM /10% FCS containing 120ng/ml CSF1. Extracted molecule: total RNA Extraction protocol: mRNA was harvested using RNeasy kit( QIAGEN) with DNase treatment on column. 1 ug of total RNA was used for the construction of sequencing libraries. RNA libraries were prepared for sequencing using standard Ion Torrent protocols

Library strategy: RNA-Seq Library source: transcriptomic Library selection: cDNA Instrument model: Ion Torrent S5

Description: Wt_48h_M24_BMM Wt_vs_IL4_allprobes_reads.txt Wt_vs_IL4_allprobes_log2_RPM.txt Data processing: Torrent Suite Software 5.10 used for basecalling and sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence- returning a fastq file (raw data) Reads were then mapped to the GRCm38.p6 genome using the open source Hisat2-2.0.5 aligner. The Hisat2 generated BAM files were uploaded into SeqMonk (version 1.42) with minimum mapping quality set to 60 The edgeR platform, within SequeMonk, was uesed to generate lists of differential gene expression from the raw reads as is required in analysis of negative binomial distributions Tab-delimited text files of all genes and differentially expressed genes (at p<0.05, p<0.01 and p<0.001) showing raw reads or log2 RPM were output (processed files) Ampliseq Torrent Suite Software 5.10 used for basecalling and sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence. Returning a fastq file (raw data) of reads associated with each of the 16000 barcoded primer pairs. Reads were then mapped to the GRCm38.p6 genome using the open source Hisat2-2.0.5 aligner. The Hisat2 generated BAM files were uploaded into SeqMonk (version 1.42) with minimum mapping quality set to 60 The edgeR platform, within SequeMonk, was uesed to generate lists of differential gene expression from the raw reads as is required in analysis of negative binomial distributions Tab-delimited text files of all genes and differentially expressed genes (at p<0.05, p<0.01 and p<0.001) showing raw reads or log2 RPM were output (processed files) Genome_build: Genome Reference Consortium mouse genome (GRCm39.p6) Supplementary_files_format_and_content: tab-delimited text files include reads or log2 RPM for each sample showing all genes or differential expression between conditions.

Sample type: SRA

Source name: bone marrow derived Organism: Mus musculus Characteristics strain: C57BL/6 Sex: male age: 8 to 10 weeks Growth protocol: After 1 day in 0.6ng/ml CSF1 in alpha+ MEM /15% FCS, non-adherent cells were incubated for a second day in a fresh dish containing 0.6ng/ml CSF1 in alpha+ MEM /15% FCS Extracted molecule total RNA Extraction protocol mRNA was harvested using RNeasy kit( QIAGEN) with DNase treatment on column. 1 ug of total RNA was used for the construction of sequencing libraries. RNA libraries were prepared for sequencing using standard Ion Torrent protocols

Library strategy: RNA-Seq Library source: transcriptomic Library selection: cDNA Instrument model: Ion Torrent Proton

Description: C1_R3 C1_C2_C3_allprobes_reads.txt C1_C2_C3_allprobes_log2_RPM.txt Data processing: Torrent Suite Software 5.10 used for basecalling and sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence- returning a fastq file (raw data) Reads were then mapped to the GRCm38.p6 genome using the open source Hisat2-2.0.5 aligner. The Hisat2 generated BAM files were uploaded into SeqMonk (version 1.42) with minimum mapping quality set to 60 The edgeR platform, within SequeMonk, was uesed to generate lists of differential gene expression from the raw reads as is required in analysis of negative binomial distributions Tab-delimited text files of all genes and differentially expressed genes (at p<0.05, p<0.01 and p<0.001) showing raw reads or log2 RPM were output (processed files) Ampliseq Torrent Suite Software 5.10 used for basecalling and sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence. Returning a fastq file (raw data) of reads associated with each of the 16000 barcoded primer pairs. Reads were then mapped to the GRCm38.p6 genome using the open source Hisat2-2.0.5 aligner. The Hisat2 generated BAM files were uploaded into SeqMonk (version 1.42) with minimum mapping quality set to 60 The edgeR platform, within SequeMonk, was uesed to generate lists of differential gene expression from the raw reads as is required in analysis of negative binomial distributions Tab-delimited text files of all genes and differentially expressed genes (at p<0.05, p<0.01 and p<0.001) showing raw reads or log2 RPM were output (processed files) Genome_build: Genome Reference Consortium mouse genome (GRCm39.p6) Supplementary_files_format_and_content: tab-delimited text files include reads or log2 RPM for each sample showing all genes or differential expression between conditions.

Male sterility is important mechanism in watermelon for production of hybrid seed. While some fruit development related studies were widely performed in watermelon, there are no reports of profiling gene expression in floral organs of watermelon. RNA-seq analysis was performed in order to identify male sterility related genes from two different groups of watermelon (genetic male-sterile (GMS) DAH3615-MS line and male-fertile DAH3615 line, respectively) to identify the differentially expressed genes (DEGs). This study employed tophat and edgeR for transcriptome analysis of next-generation RNA-seq data, which included 2 tissues obtained from 2 different breeds of watermelon

Sample type: SRA

Source name: bone marrow derived Organism: Mus musculus Characteristics
strain: C57BL/6 Sex: male age: 8 to 10 weeks Growth protocol: After 1 day in 0.6ng/ml CSF1 in alpha+ MEM /15% FCS, non-adherent cells were incubated for two days in a fresh dish containing 12ng/ml CSF1 in alpha+ MEM /10% FCS and then for 7 days in 120ng/ml CSF1 in alpha+ MEM /10% FCS. Cells were incubated for a further two days in fresh alpha+ MEM /10% FCS containing 120ng/ml CSF1. Extracted molecule: total RNA Extraction protocol: mRNA was harvested using RNeasy kit( QIAGEN) with DNase treatment on column. 1 ug of total RNA was used for the construction of sequencing libraries. RNA libraries were prepared for sequencing using standard Ion Torrent protocols

Library strategy: RNA-Seq Library source: transcriptomic Library selection: cDNA Instrument model: Ion Torrent S5

Description: Wt_48h_M7_BMM Wt_vs_IL4_allprobes_reads.txt Wt_vs_IL4_allprobes_log2_RPM.txt Data processing: Torrent Suite Software 5.10 used for basecalling and sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence- returning a fastq file (raw data) Reads were then mapped to the GRCm38.p6 genome using the open source Hisat2-2.0.5 aligner. The Hisat2 generated BAM files were uploaded into SeqMonk (version 1.42) with minimum mapping quality set to 60 The edgeR platform, within SequeMonk, was uesed to generate lists of differential gene expression from the raw reads as is required in analysis of negative binomial distributions Tab-delimited text files of all genes and differentially expressed genes (at p<0.05, p<0.01 and p<0.001) showing raw reads or log2 RPM were output (processed files) Ampliseq Torrent Suite Software 5.10 used for basecalling and sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence. Returning a fastq file (raw data) of reads associated with each of the 16000 barcoded primer pairs. Reads were then mapped to the GRCm38.p6 genome using the open source Hisat2-2.0.5 aligner. The Hisat2 generated BAM files were uploaded into SeqMonk (version 1.42) with minimum mapping quality set to 60 The edgeR platform, within SequeMonk, was uesed to generate lists of differential gene expression from the raw reads as is required in analysis of negative binomial distributions Tab-delimited text files of all genes and differentially expressed genes (at p<0.05, p<0.01 and p<0.001) showing raw reads or log2 RPM were output (processed files) Genome_build: Genome Reference Consortium mouse genome (GRCm39.p6) Supplementary_files_format_and_content: tab-delimited text files include reads or log2 RPM for each sample showing all genes or differential expression between conditions.

This page includes the data and code necessary to reproduce the results of the following paper: Yang Liao, Dinesh Raghu, Bhupinder Pal, Lisa Mielke and Wei Shi. cellCounts: fast and accurate quantification of 10x Chromium single-cell RNA sequencing data. Under review. A Linux computer running an operating system of CentOS 7 (or later) or Ubuntu 20.04 (or later) is recommended for running this analysis. The computer should have >2 TB of disk space and >64 GB of RAM. The following software packages need to be installed before running the analysis. Software executables generated after installation should be included in the $PATH environment variable.

R (v4.0.0 or newer) https://www.r-project.org/ Rsubread (v2.12.2 or newer) http://bioconductor.org/packages/3.16/bioc/html/Rsubread.html CellRanger (v6.0.1) https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome STARsolo (v2.7.10a) https://github.com/alexdobin/STAR sra-tools (v2.10.0 or newer) https://github.com/ncbi/sra-tools Seurat (v3.0.0 or newer) https://satijalab.org/seurat/ edgeR (v3.30.0 or newer) https://bioconductor.org/packages/edgeR/ limma (v3.44.0 or newer) https://bioconductor.org/packages/limma/ mltools (v0.3.5 or newer) https://cran.r-project.org/web/packages/mltools/index.html

Reference packages generated by 10x Genomics are also required for this analysis and they can be downloaded from the following link (2020-A version for individual human and mouse reference packages should be selected): https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest After all these are done, you can simply run the shell script ‘test-all-new.bash’ to perform all the analyses carried out in the paper. This script will automatically download the mixture scRNA-seq data from the SRA database, and it will output a text file called ‘test-all.log’ that contains all the screen outputs and speed/accuracy results of CellRanger, STARsolo and cellCounts.

Supplementary table 2, containing full results from edgeR RNA-sequencing differential expression analysis of B. malayi microfilariae controls compared to treatment with tetracyclines, used in the associated manuscript for drawing conclusions. Full details on data generation are available in the related manuscript.

Nrp2fl/flLyz2-cre and Nrp2fl/fl mice were administered drinking water or 2.5% DSS for 7 days to establish acute experimental colitis model. At day 7, colon tissues from Nrp2fl/flLyz2-cre and Nrp2fl/fl mice (n=3 per group) were isolated and CX3CR1+ cells were enriched with corresponding selection kit following the manufacture’s instruction for RNA sequencing analysis. All the animal experiments were approved by Institutional Animal Care and Use Committee of Sichuan University.UID RNA-seq experiment and high through-put sequencing and data analysis were conducted by Seqhealth Technology Co., LTD (Wuhan, China). Briefly, total RNAs were extracted from samples using TRIzol Reagent. 2 μg total RNAs were used for stranded RNA sequencing library preparation using KC-DigitalTM Stranded mRNA Library Prep Kit for Illumina® following the manufacturer’s instruction. Raw sequencing data was first filtered by Trimmomatic (version 0.36). Low-quality reads were discarded, and the reads contaminated with adaptor sequences were trimmed. Clean Reads were further treated with in-house scripts to eliminate duplication bias introduced in library preparation and sequencing. The de-duplicated consensus sequences were used for standard RNA-seq analysis. Reads mapped to the exon regions of each gene were counted by featureCounts (Subread-1.5.1; Bioconductor) and then RPKM was calculated. Genes differentially expressed between groups were identified using the edgeR package (version 3.12.1). A p-value cutoff of 0.05 and Fold-change cutoff of 2 were used to judge the statistical significance of gene expression differences.

In addition to the nucleic acid, plasma membrane also found on a variety of biological molecules.However, the researchers have realized the RNA with in vitro with membrane vesicles and connect to the cytoplasmic membrane.Therefore, the combination of high throughput sequencing and is suitable for marking method provide the mass appraisal method of extracellular RNA.Here, we applied the APEX of a recently published - seq method, and identified 75 plasma membrane of RNA, which lncRNA PMAR72 located around the film.In addition, we observed that focus on a specific membrane PMAR72 lesions in the territory.Our findings will provide in this paper, the RNA and mammals, the relationship between the cytoplasm membrane to provide some new evidence.Overall design: markup is generated for the following structure goal and unmarked contrast APEX - seq library: Lck (internal), Chinese (internal), CD28 (external), PDGFR (external).For each build body six to eight library (mark targets have 3 biological reproduction, not tag comparison with three biological replication).Using the Illumina VAHTS Fast RNA - seq Library Prep Kit, preparation of RNA from the concentration of total RNA - seq Library.By pairing terminal sequencing in Illumina X10 on library sequencing.Data has carried on the strict quality control, and further use Trimmomatics v0.38 pretreatment.Then use the HISAT v2.1.0 readings than to hg38 genome.FPKM calculated by Rsubread and edgeR.Enrichment factor change and p values by quantitative edgeR and negative binomial model.

MicroRNAs (miRNAs) could play an important role as potential Alzheimer Disease (AD) biomarkers. Plasma samples were collected from participants: Mild cognitive impairment (MCI) due to AD patients (n= 20), preclinical AD patients (n= 8) and healthy controls (n= 20). Then, small RNA sequencing analysis, followed by miRNA differential expression analysis comparing different methods (DESeq2, edgeR, NOISeq) were carried out.