One of the key challenges for transcriptomics-based research is not only the processing of large data but also modeling the complexity of features that are sources of variation across samples, which is required for an accurate statistical analysis. Therefore, our goal is to foster access for wet lab researchers to bioinformatics tools, in order to enhance their ability to explore biological aspects and validate hypotheses with robust analysis. In this context, user-friendly interfaces can enable researchers to apply computational biology methods without requiring bioinformatics expertise. Such bespoke platforms can improve the quality of the findings by allowing the researcher to freely explore the data and test a new hypothesis with independence. Simplicity DiffExpress is a data-driven software platform dedicated to enabling non-bioinformaticians to take ownership of the differential expression analysis (DEA) step in a transcriptomics experiment while presenting the results in a comprehensible layout, which supports an efficient results exploration, information storage, and reproducibility. Simplicity DiffExpress’ key component is the bespoke statistical model validation that guides the user through any necessary alteration in the dataset or model, tackling the challenges behind complex data analysis. The software utilizes edgeR, and it is implemented as part of the SimplicityTM platform, providing a dynamic interface, with well-organized results that are easy to navigate and are shareable. Computational biologists and bioinformaticians can also benefit from its use since the data validation is more informative than the usual DEA resources. Wet-lab collaborators can benefit from receiving their results in an organized interface. Simplicity DiffExpress is freely available for academic use, and it is cloud-based (https://simplicity.nsilico.com/dea).

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 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.

One of the fundamental aspects of genomic research is the identification of differentially expressed (DE) genes between two conditions. In the past decade, numerous DE analysis tools have been developed, employing various normalization methods and statistical modelling approaches. In this article, we introduce DElite, an R package that leverages the capabilities of four state-of-the-art DE tools: edgeR, limma, DESeq2, and dearseq. DElite returns the outputs of the four tools with a single command line, thus providing a simplified way for non-expert users to perform DE analysis. Furthermore, DElite provides a statistically combined output of the four tools, and in vitro validations support the improved performance of these combination approaches for the detection of DE genes in small datasets. Finally, DElite offers comprehensive and well-documented plots and tables at each stage of the analysis, thus facilitating result interpretation. Although DElite has been designed with the intention of being accessible to users without extensive expertise in bioinformatics or statistics, the underlying code is open source and structured in such a way that it can be customized by advanced users to meet their specific requirements. DElite is freely available for download from https://gitlab.com/soc-fogg-cro-aviano/DElite.

A comparative metatranscriptomic approach was applied to assess the taxonomic and functional characteristics of the rhizosphere microbiome of wheat plants grown in adjacent fields which are suppressive and non-suppressive to the plant pathogen, R. solani AG8.The paper on this research has now been published: Hayden, H. L., K. W. Savin, J. Wadeson, V. V. S. R. Gupta and P. M. Mele (2018). Comparative Metatranscriptomics of Wheat Rhizosphere Microbiomes in Disease Suppressive and Non-suppressive Soils for Rhizoctonia solani AG8. Frontiers in Microbiology 9(859).https://doi.org/10.3389/fmicb.2018.00859 The files in this set include:1. Metatranscriptome assembly - TrinityAllMRZincrRNA.fasta.gz2. Trinotate generated annotation file for the metatranscriptome assembly - Trinotate_report_wAnnotsOnly.txt3. The count matrix generated in Trinity using RSEM for differential expression analysis. 4. BLASTN of NCBI nt database (E ≤ 1e-5) annotation file for differentially expressed genes as identified using EdgeR- de.7219.Trinity.isoforms.srtXlogFC.nt.tophits.txt 5. BLASTX of NCBI nr database (E ≤ 1e-5) annotation file for differentially expressed genes as identified using EdgeR- de.7219.Trinity.isoforms.srtXlogFC.nr.tophits.txt6. Testing of differential expression software EdgeR compared to DeSeq2 on the metatranscriptome counts matrix-EdgeR&DeSeq2Analysis.docx Trinity (version 2.2.0) was used for de novo metatranscriptome assembly. A set of 348,722,194 quality filtered reads from 12 rhizosphere libraries (six suppressive soil, six non-suppressive soil) was combined into a single reference transcriptome assembly. The assembly was annotated using Trinotate. Transcripts were subjected to a BLASTX search (E ≤ 1e-5) of the protein database Swiss-Prot downloaded from UniProt (http://www.uniprot.org/). The software Transdecoder (http://transdecoder.github.io) was used to predict likely coding regions within transcripts, and resulting protein products were subjected to a BLASTP search (E ≤ 1e-5) against the Swiss-Prot database. To identify conserved protein domains we used Hmmer software (http://hmmer.org/) and PFam. KEGG, Gene Ontology (GO), and Eggnog annotations were retrieved from Swiss-Prot where transcripts could be assigned to these databases. All results for the reference assembly annotation were parsed by Trinotate, stored in a SQLite database and then reported as a tab-delimited summary file. Only contigs with annotations are reported in the attached file. A count matrix produced in Trinity using RSEM was used for differential expression analysis in edgeR. For the assembly transcript abundance was filtered at a count per million (CPM) of 0.5 though expression was required in five of the six replicate samples. Normalisation to allow comparison between samples was performed for each count table in edgeR using TMM (trimmed mean of M-values). EdgeR settings included using the generalised linear model (GLM) likelihood ratio test with the contrast option (suppressive minus non-suppressive). Differentially expressed transcripts from the Trinity assembly were also subject to BLASTX and BLASTN searches of Genbank (E ≤ 1e-5).Exploration was done to examine the numbers and types of of contigs identified as being differentially expressed when the software DESeq2 was used for differential gene expression analysis, in comparison to the dataset above produced by EdgeR analyses.

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 ...

Becker muscular dystrophy (BMD) is a rare X-linked recessive neuromuscular disorder, frequently caused by in-frame deletions in the DMD gene that result in the production of a truncated, yet functional, dystrophin protein. The consequences of BMD-causing in-frame deletions on the organism are difficult to predict, especially in regard to long-term prognosis. Here, we used CRISPR-Cas9 to generate a new Dmd Δ52-55 mouse model by deleting exons 52-55 in the Dmd gene, resulting in a BMD-like in-frame deletion. To delineate the long-term effects of this deletion, we studied these mice over 52 weeks by performing histology and echocardiography analyses and assessing motor functions. To further delineate the effects of the exons 52-55 in-frame deletion, we performed RNA-Seq pre- and post-exercise and identified several differentially expressed pathways that could explain the abnormal muscle phenotype observed at 52 weeks in the BMD model.

This dataset shows the results and raw data of the RNA-sequencing and transcriptomic analysis for 52-week-old exercised and non-exercised mice (4 BMD, 4 WT and 4 DMD, as mentioned on the names of each file).

1. Due to size restrictions, this RNA-Seq dataset will be published on Zenodo in 3 parts. This first part contains the data for the exercised mice, including the fastq (R1 and R2) and associated (md5) files for the 4 BMD mice (15315-15318) and 2 DMD mice (15319 and 15320), all the raw gene counts (txt files), and all the differentially expressed genes (tsv files).

Workflow (performed by TCAG at SickKids):

2. RNA-Seq Library and Reference Genome Information

Type of library: stranded, paired end

Genome reference sequence: GRCm39, M31 Gencode gene models.

3. Read Pre-processing, Alignment and Obtaining Gene Counts

3.1 Read Pre-processing

The sequencing data is in FASTQ format. The quality of the data is assessed using FastQC v.0.11.5 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).

Adaptors are trimmed using Trim Galore (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) v. 0.5.0. Trim Galore is running Cutadapt (https://cutadapt.readthedocs.org/en/stable/) v. 1.10. Trim Galore is run with the following parameters:

-q 25 – the reads are trimmed from the 3' end base by base, trimming stops if the quality of the base is greater than 25;

--clip_R1 6, --clip_R2 6 – clip the first 6 nucleotides from the 5' ends of read 1 and read 2;

--stringency 5 – at least 5 nucleotides overlap with the Illumina primer sequence are needed for trimming;

--length 40 – any read that is shorter than 40 nucleotides as a result of trimming is discarded;

--paired – only pairs of reads are retained (for paired-end reads only, not for single reads).

The type of adaptor is automatically detected by screening the first 1 million sequences of the first specified file for the first 12/13 nucleotides of the standard Illumina or Nextera primers and the sequence from the start of the primer to the 3' end of the read is trimmed.

The quality of the trimmed reads is re-assessed with FastQC.

The trimmed reads are also screened for presence of rRNA and mtRNA sequences using FastQ-Screen v.0.10.0 (http://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/).

To assess the read distribution, positional read duplication and to confirm the strandedness of the alignments we use the RSeQC package (http://rseqc.sourceforge.net/), v. 2.6.2. The distribution of reads across exonic, intronic and intergenic sequences is assessed by the read_distribution.py program, infer_experiment.py is used for confirming strandedness, and read_duplication.py is used to obtain the positional read duplication (percentage of reads mapping to exactly the same genomic location). Sufficient proportion of reads should map to the exonic sequences (ideally > 70-80%). Large amounts of reads mapping to intronic sequences in a poly-A mRNA library will suggest significant presence of pre-mRNA or other issues with RNA preparation. For stranded RNA-seq experiments the majority of the reads should map exclusively to one strand, same or opposite to the transcript, depending on the library preparation method. For non-stranded experiments the reads should be equally distributed to both strands.

3.2. Read Alignment

The raw trimmed reads are aligned to the reference genome using the STAR aligner, v.2.6.0c. (https://github.com/alexdobin/STAR, https://academic.oup.com/bioinformatics/article/29/1/15/272537). The alignments are contained in the .bam files. The “.bam” together with the “.bai” files can be used for viewing of the alignments in the Integrative Genomics Viewer (IGV, http://software.broadinstitute.org/software/igv/).

3.3. Obtaining Gene Counts

The filtered STAR alignments are processed to extract raw read counts for genes using htseq-count v.0.6.1p2 (HTSeq, http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html). Assigning reads to genes by htseq-count is done in the mode “intersection_nonempty”, i.e. if a read overlaps with two overlapping genes and the overlap to gene A is greater than the overlap to gene B, the read is counted towards gene A, while if a read overlaps equally with gene A and gene B, then it is not counted towards either gene. Htseq_count does not count reads with multiple alignments to avoid introducing bias in the expression results. Only uniquely mapping reads are counted.

4. Pre-processing, Alignment and Gene Counts QC

MultiQC (https://multiqc.info/) is a reporting tool that aggregates statistics generated by bioinformatics analyses across multiple samples. MultiQC v. 1.14 was used to generate a consolidated report from FastQC screening of both untrimmed and trimmed reads, and from RSeQC, FastQ Screen, STAR and htseq-count results. The MultiQC report is contained in MultiQC_Report_*.html file.

5. DGE Analysis with edgeR

Differential expression was done with the edgeR R package v.3.28.1, using R v.3.6.1 (http://www.bioconductor.org/packages/release/bioc/html/edgeR.html). The data set was filtered to retain only genes whose gene counts were >50 in at least 3 samples. This is intended to remove genes that are notexpressed, or expressed at a very low level.

The method used for normalizing the data was TMM, implemented by the calcNormFactors(y) function. All samples were normalized and filtered together. The glmLRT functionality in edgeR was used for the differential expression tests, with sample group taken into account.

EdgeR Results Legend:

· GeneID – Ensembl Gene ID;

· Chr.Start.End - gene coordinates;

· GeneName, GeneType, etc. – Gene attributes, derived from the genome annotation;

· logFC - Log2 Fold Change (use this column for selection of DEGs);

· logCPM - Log2 Counts Per Million, average for all libraries;

· LR – Statistic calculated by the LR-Test;

· PValue - Differential expression P value;

· FDR – Differential expression False Discovery Rate, calculated by the Benjamini-Hochberg method (use this column for selection of DEGs);

· (columns labeled with sample names) – Fragments Per Kilobase of transcript per Million mapped reads (FPKMs) for the given samples.

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 and 20ng/ml IL4. 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: IL4_48h_M26_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.

One of the fundamental aspects of genomic research is the identification of differentially expressed (DE) genes between two conditions. In the past decade, numerous DE analysis tools have been developed, employing various normalization methods and statistical modelling approaches. In this article, we introduce DElite, an R package that leverages the capabilities of four state-of-the-art DE tools: edgeR, limma, DESeq2, and dearseq. DElite returns the outputs of the four tools with a single command line, thus providing a simplified way for non-expert users to perform DE analysis. Furthermore, DElite provides a statistically combined output of the four tools, and in vitro validations support the improved performance of these combination approaches for the detection of DE genes in small datasets. Finally, DElite offers comprehensive and well-documented plots and tables at each stage of the analysis, thus facilitating result interpretation. Although DElite has been designed with the intention of being accessible to users without extensive expertise in bioinformatics or statistics, the underlying code is open source and structured in such a way that it can be customized by advanced users to meet their specific requirements. DElite is freely available for download from https://gitlab.com/soc-fogg-cro-aviano/DElite.

Ocular toxoplasmosis is the commonest clinical manifestation of infection with obligate intracellular parasite, Toxoplasma gondii. Active ocular toxoplasmosis is characterized by replication of T. gondii tachyzoites in the retina, with reactive inflammation. The multifunctional retinal pigment epithelium is a key target cell population for T. gondii. Since the global gene expression profile is germane to understanding molecular involvements of retinal pigment epithelial cells in ocular toxoplasmosis, we performed RNA-Sequencing (RNA-Seq) of human cells following infection with T. gondii tachyzoites. Primary cell isolates from eyes of cadaveric donors (n = 3), and the ARPE-19 human retinal pigment epithelial cell line, were infected for 24 h with GT-1 strain T. gondii tachyzoites (multiplicity of infection = 5) or incubated uninfected as control. Total and small RNA were extracted from cells and sequenced on the Illumina NextSeq 500 platform; results were aligned to the human hg19 reference sequence. Multidimensional scaling showed good separation between transcriptomes of infected and uninfected primary cell isolates, which were compared in edgeR software. This differential expression analysis revealed a sizeable response in the total RNA transcriptome—with significantly differentially expressed genes totaling 7,234 (28.9% of assigned transcripts)—but very limited changes in the small RNA transcriptome—totaling 30 (0.35% of assigned transcripts) and including 8 microRNA. Gene ontology and pathway enrichment analyses of differentially expressed total RNA in CAMERA software, identified a strong immunologic transcriptomic signature. We conducted RT-qPCR for 26 immune response-related protein-coding and long non-coding transcripts in epithelial cell isolates from different cadaveric donors (n = 3), extracted by a different isolation protocol but similarly infected with T. gondii, to confirm immunological activity of infected cells. For microRNA, increases in miR-146b and miR-212 were detected by RT-qPCR in 2 and 3 of these independent cell isolates. Biological network analysis in the InnateDB platform, including 735 annotated differentially expressed genes plus 2,046 first-order interactors, identified 10 contextural hubs and 5 subnetworks in the transcriptomic immune response of cells to T. gondii. Our observations provide a solid base for future studies of molecular and cellular interactions between T. gondii and the human retinal pigment epithelium to illuminate mechanisms of ocular toxoplasmosis.

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 and 20ng/ml IL4. 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: IL4_48h_M10_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.

Aims and Methods:To identify the potential genes or pathways involved in ROS generation following 1-d SD, we collected tissues containing the head and gut from WT Drosophila for RNA sequencing (RNA-seq). And to gain more insights into the processes involved in RET-ROS and humoral response of hemocytes during SD, we conducted RNA-seq of hemocytes harvested from non-SD and 1-day SD groups. Hemocyte samples were collected from approximately 60 adult female flies (Figure 5E). For hemocyte isolation, 20 flies were loaded onto a 30 μm filter in a Mobicol spin column (MOBITEC), covered with glass beads, and centrifuged at 10,000 rpm for 20 min at 4°C. This procedure was repeated twice using a total of 40 flies, with recovered hemolymph pooled in 300 μl TRIzol reagent. Head-gut samples consisted of 50 head-gut complexes in 500 μl TRIzol (Figure 2A). Total RNA was extracted using the UNlQ-10 Column Trizol Total RNA Isolation Kit (Sangon Biotech) followed by DNaseI (BBI) treatment. RNA purification employed Oligo dT magnetic beads, with final samples stored at -80°C until analysis. For RNA-seq, cDNA libraries were prepared with the Illumina TruSeq stranded mRNA library prep kit and sequenced on an Illumina platform using paired-end (PE) sequencing. Raw reads were processed to obtain clean reads by removing contaminants, duplicates, and low-quality sequences. Transcriptome analysis included: (1) quality control, (2) alignment analysis, and (3) in-depth annotation including Multi-platform Gene Ontology (GO). Differential expression was determined using volcano plot filtering |log2-fold change| ≥ 0.58 (≥1.5-fold changes) and false discovery rate (FDR) < 0.05.Files: The file names started with Non-SD or SD are the data file from hemocytes (12 files in total, with 6 Non-SD and 6 SD samples), while the others are from head+gut tissues (12 files in total, with 6 Non-SD and 6 SD samples). Software: Initially, in the quality control (QC) phase, Cutadapt (version V1.9.1) and FastQC (version V0.10.1) are employed. Moving on to the mapping stage, HISAT2 (version V2.2.1) is used for sequence alignment. Gene expression analysis is conducted with HTSEQ (version V0.6.1). Transcript assembly is performed using Stringtie (version V1.3.3b). For differential expression analysis, three different software tools are utilized: DESeq2 (version V1.26.0), DESeq (version V1.38.0), and EdgeR (version V3.28.1). Additionally, Cuffdiff (version V2.2.1) is also employed for this analysis. Finally, in the GO enrichment analysis, GOSeq (version V1.34.1) and TopGO (version V2.18.0) are the software tools used.

Automatic Optical Lens Edger Market Outlook

The global automatic optical lens edger market size is projected to grow significantly, with an estimated valuation of USD 320 million in 2023 and a forecasted valuation of USD 530 million by 2032, reflecting a healthy CAGR of 5.5% over the forecast period. The growth of this market is driven by increasing demand for precision in lens edging, advancements in optical technology, and the rising adoption of digital eye care solutions. The ever-evolving landscape of vision correction and the growing need for customized lenses are further propelling this market, as manufacturers and end-users alike seek efficient and accurate lens edging solutions. The continuous advancements in lens edger technology, such as automation and digital integration, are expected to fuel this growth trajectory, making the market a lucrative segment within the optical industry.

The increasing prevalence of vision-related disorders is one of the primary growth factors affecting the automatic optical lens edger market. As the global population ages, the incidence of eye conditions such as myopia, hyperopia, and astigmatism is on the rise, leading to a higher demand for corrective lenses. This trend is particularly pronounced in developed regions with aging demographics, as well as in emerging markets where access to vision care is expanding. Moreover, the increasing emphasis on regular eye check-ups and the adoption of vision insurance plans are encouraging consumers to invest in high-quality lenses, which in turn fuels the demand for advanced lens edging machines. The integration of cutting-edge technologies, such as computer-aided designs and advanced materials, is further enhancing the precision and efficiency of automatic lens edgers, thereby driving market growth.

Advancements in digital lens technology are another significant driver for the automatic optical lens edger market. The shift from traditional to digital lens technology has revolutionized the optical industry, enabling the production of high-precision lenses with customized features. Automatic optical lens edgers are integral to this process, as they facilitate the precise cutting and shaping of lenses to meet specific optical prescriptions. The demand for digital lenses, including progressive lenses and anti-reflective coatings, is surging as consumers seek lenses that offer superior vision correction and aesthetic appeal. As optical retailers and laboratories strive to meet these demands, the adoption of advanced lens edging systems is becoming increasingly essential, providing a substantial impetus to market growth.

The growing trend of automation in optical labs and retail stores is also contributing to the expansion of the automatic optical lens edger market. Automation enhances operational efficiency by reducing manual labor and minimizing errors, which are crucial in the optical industry where precision is paramount. Automatic lens edgers are equipped with sophisticated software and hardware components that streamline the lens production process, ensuring high accuracy and consistency in lens edging. This technological advancement is particularly appealing to optical retailers and laboratories, which are looking to improve productivity, reduce costs, and enhance customer satisfaction. The integration of IoT and digital connectivity in these devices is further boosting their appeal, making them indispensable tools in the modern optical landscape.

In the realm of optical technology, the role of an Optical Lens Groover is becoming increasingly significant. These devices are crucial in the precise crafting of lens edges, particularly for rimless eyewear, where the groove is essential for securing the lens to the frame. As the demand for sleek and minimalist eyewear designs grows, the need for advanced grooving technology becomes more pronounced. Optical Lens Groovers offer the precision and efficiency required to meet these design specifications, ensuring that lenses not only fit perfectly but also maintain their structural integrity. This technology is particularly beneficial in high-volume optical labs, where speed and accuracy are paramount to meeting consumer demands.

Regionally, the growth of the automatic optical lens edger market is notably robust in the Asia Pacific, where the burgeoning middle class and increasing access to vision care services are driving demand. Countries like China and India, with their large population bases and rising disposable incomes, present significant growth opportunities for market participants. Additiona

We have previously identified that T cell tolerance is the stopgap to red blood cell (RBC)-specific autoimmunity. We have also shown that while tolerance is robust when mice are young, it fails upon age and leads to development of RBC autoantibodies. Herein, we set out to determine which transcripts were associated with tolerization of RBC-autoreative CD4 T cells. For these studies, we used a murine model of autoimmune hemolytic anemia (AIHA), whereby HOD mice (expressing a known RBC-specific antigen) were bred with OTII animals (which react to the ovalbumin epitope contained within teh HOD antigen) [see PMCID PMC8634489]. To that end, splenocytes were collected from 10-12 week old HODxOTII F1 mice (n=4 each genotype) and stained with antibodies against Thy1.2, Ter119, CD19, and CD4. CD19+ B cells and Ter119+ RBCs cells were excluded from live singlets and CD4+Thy1.2+ T cells were sorted. Total RNA was harvested using RNeasy mini plus kit (Qiagen). Samples were sent to the Genome Techno..., Samples were prepared according to library kit manufacturer's protocol, indexed, pooled, and sequenced on an Illumina HiSeq. Data analysis was performed by TAC Genomics (https://tacgenomics.com). The reads were first mapped to the latest UCSC transcript set using Bowtie2 version 2.1.0 and the gene expression level was estimaed using RSEM v1.2.15. Differentially expressed genes were identified using the edgeR program. Genes showing altered expression with p < 0.05 and more than 1.5 fold changes were considered differentially expressed. Goseq was used to perform the GO enrichment analysis and Kobas was used to perform the pathway analysis., , # HODxOTII CD4 T cell RNAseq

https://doi.org/10.5061/dryad.5qfttdzdf

CD4+ T cells were sorted from HODxOTII F1 mice on a C57Bl/6J background. HOD is a model antigen containing hen egg lysosome, ovalbumin, and human blood group molecule Duffy. HOD is expressed in a red blood cell (RBC)-specific fashion. CD4 OTII T cells react to an epitope within ovalbumin. Thus, HOD+OTII+ mice have RBC autoreactive CD4 T cells. For these studies, CD4 T cells were sorted from autoreactive HOD+OTII+ and littermate HOD-OTII+ negative controls. The goal of the RNAseq was to determine which genes were associated with CD4 T cells tolerance to RBC-specific autoantigens.

There are 8 total samples: HOD+OTII+ (n=4) and HOD-OTII+ (n=4). Each sample was run in 3 lanes, with paired-end reads. As such, there are 6 Fastq files (where 5_1/5_4, 6_1/6_4, 7_1/7_4 are paired reads). Each raw data file has been uploaded with the genotype and mouse number in the file name.Â

Indi...

Links of differentially expressed genes identified by Cufflinks and EdgeR programs to BarleyCyc pathways. (XLSX 25Â kb)

Infection tolerance in rodents was examined by injecting single-dose lipopolysaccharide (LPS) to induce inflammation in Peromyscus leucopus (LL stock), the white-footed deermouse also reservoir for Lyme disease and Mus musculus (outbred CD-1 breed), the house mouse, and Rattus norvegicus, the brown rat (Fischer strain). Reaction to LPS was analyzed in the blood of challenged rodents and compared to control animals. As natural reservoirs of zoonoses deermice show significant anti-inflammatory response as described in "An Infection-Tolerant Mammalian Reservoir for Several Zoonotic Agents Broadly Counters the Inflammatory Effects of Endotoxin" (https://doi.org/10.1128/mBio.00588-21). The project and the description of the samples are described under the following NCBI BioProjects: PRJNA975149 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA975149) for mouse and deermouse and PRJNA973677 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA973677). This project is a follow-up project focusing on the..., Peromyscus leucopus (10 animals, 5 of each sex) and M. musculus (10 animals, 5 of each sex) were injected into the peritoneum with the solution of purified E. coli lipopolysaccharide (10 microgram/gram of body weight). Same number of animals was injected with saline and used as controls. The rats were studied in a separate experiment with 5 control animals and 6 animals receiving 5 micrograms LPS per gram of body weight and 5 animals receiving 20 micrograms LPS per gram. All animals were euthanized 4 hours post-injection. Blood from each animal was used for extracting RNA and processed further using Novaseq Illumina technology with paired-end chemistry and 150 cycles for mice and deermice and 100 cycles for rats providing ~ 50 million reads per sample. After sequencing reads were trimmed and transcripts annotated using Genomics Workbench v. 23. Differential gene expression (DEG) was assessed using the same suite of software and a modification of EdgeR method. Sequenced data was deposite..., The data is in CSV tabular data format and can be opened as a text file or as a spreadsheet., GENERAL INFORMATION

• Title of Dataset: Differentially-expressed genes in Peromyscus leucopus, Mus musculus, and Rattus norvegicus blood in response to LPS

• Author Information

  A. Principal Investigator Contact Information

  Name: Alan G. Barbour; Institution: University of California Irvine; Address: 843 Health Sciences Court, Irvine, CA 92697; Email: abarbour@uci.edu

  B. Associate or Co-investigator Contact Information Name: Ana Milovic; Institution: University of California Irvine; Address: 843 Health Sciences Court, Irvine, CA 92697; Email: milovica@uci.edu

• Date of data collection (single date, range, approximate date): 2017-2023

• Geographic location of data collection: Irvine, California, USA

• Information about funding sources that supported the collection of the data: National Institute of Allergy and Infectious Diseases grants AI-136523 and AI-157513

SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: None
2. Link...

Mitochondrial functions are intimately reliant on proteins and RNAs encoded in both the nuclear and mitochondrial genomes, leading to inter-genomic coevolution within taxa. Hybridization can break apart coevolved mitonuclear genotypes, resulting in decreased mitochondrial performance and reduced fitness. This hybrid breakdown is an important component of outbreeding depression and early-stage reproductive isolation. However, the mechanisms contributing to mitonuclear interactions remain poorly resolved. Here we scored variation in developmental rate (a proxy for fitness) among reciprocal F2 inter-population hybrids of the intertidal copepod Tigriopus californicus, and used RNA sequencing to assess differences in gene expression between fast- and slow-developing hybrids. In total, differences in expression associated with developmental rate were detected for 2,925 genes, whereas only 135 genes were differentially expressed as a result of differences in mitochondrial genotype. Up-regulate..., Developmental rate data - collected by daily monitoring naupliar (larval) development of individual Tigriopus californicus until stage 1 copepodid metamorphosis was observed RNA-seq count data - collected by isolated RNA from pools of fast- or slow-developing Tigriopus californicus copepodids from reciprocal hybrids lines; RNA was sequenced on a NovaSeq 6000; reads were mapped to a hybrid reference genome and counted using STAR and featureCounts; differences in gene expression were tested with edgeR; gene ontology enrichments among differentially expressed genes were assessed with goseq., All files can be opened in text editors, spreadsheet programs or the statistical software R.

BackgroundIn Latin America, the bloodsucking bugs Triatominae are vectors of Trypanosoma cruzi, the parasite that causes Chagas disease. Chemical elimination programs have been launched to control Chagas disease vectors. However, the disease persists because native vectors from sylvatic habitats are able to (re)colonize houses—a process called domiciliation. Triatoma brasiliensis is one example. Because the chemosensory system allows insects to interact with their environment and plays a key role in insect adaption, we conducted a descriptive and comparative study of the chemosensory transcriptome of T. brasiliensis samples from different ecotopes.Methodology/Principal FindingIn a reference transcriptome built using de novo assembly, we found transcripts encoding 27 odorant-binding proteins (OBPs), 17 chemosensory proteins (CSPs), 3 odorant receptors (ORs), 5 transient receptor potential channel (TRPs), 1 sensory neuron membrane protein (SNMPs), 25 takeout proteins, 72 cytochrome P450s, 5 gluthatione S-transferases, and 49 cuticular proteins. Using protein phylogenies, we showed that most of the OBPs and CSPs for T. brasiliensis had well supported orthologs in the kissing bug Rhodnius prolixus. We also showed a higher number of these genes within the bloodsucking bugs and more generally within all Hemipterans compared to the other species in the super-order Paraneoptera. Using both DESeq2 and EdgeR software, we performed differential expression analyses between samples of T. brasiliensis, taking into account their environment (sylvatic, peridomiciliary and domiciliary) and sex. We also searched clusters of co-expressed contigs using HTSCluster. Among differentially expressed (DE) contigs, most were under-expressed in the chemosensory organs of the domiciliary bugs compared to the other samples and in females compared to males. We clearly identified DE genes that play a role in the chemosensory system.Conclusion/SignificanceChemosensory genes could be good candidates for genes that contribute to adaptation or plastic rearrangement to an anthropogenic system. The domiciliary environment probably includes less diversity of xenobiotics and probably has more stable abiotic parameters than do sylvatic and peridomiciliary environments. This could explain why both detoxification and cuticle protein genes are less expressed in domiciliary bugs. Understanding the molecular basis for how vectors adapt to human dwellings may reveal new tools to control disease vectors; for example, by disrupting chemical communication.

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.

Lists of differentially expressed genes. Excel (.xlsx) file of the differentially expressed genes (DEGs) identified by the edgeR software from the six contrasts of the transcriptomic analysis. Results for each contrast are on a separate tab (6 tabs in total). (XLSX 656 kb)