Project description:Purpose: Deconstructing the soil microbiome into reduced-complexity functional modules represents a novel method of microbiome analysis. The goals of this study are to confirm differences in transcriptomic patterns among five functional module consortia. Methods: mRNA profiles of 3 replicates each of functional module enrichments of soil inoculum in M9 media with either 1) xylose, 2) n-acetylglucosamine, 3) glucose and gentamycin, 4) xylan, or 5) pectin were generated by sequencing using an Illumina platform (GENEWIZ performed sequencing). Sequence reads that passed quality filters were aligned to a soil metagenome using Burrows Wheeler Aligner. Resulting SAM files were converted to raw reads using HTSeq, and annotated using Uniref90 or EGGNOG databases. Results: To reduce the size of the RNA-Seq counts table and increase its computational tractability, transcripts containing a minimum of 75 total counts, but no more than 3 zero counts, across the 15 samples were removed. The subsequent dataset was normalized using DESeq2, resulting in a dataset consisting of 6947 unique transcripts across the 15 samples, and 185,920,068 reads. We identified gene categories that were enriched in a sample type relative to the overall dataset using Fisher’s exact test. Conclusions: our dataset confirms that the functional module consortia generated from targeted enrichments of a starting soil inoculum had distinct functional trends by enrichment type.
2020-01-14 | GSE143587 | GEO
Project description:Raw sequence reads from clinical human samples
Project description:We performed the RNA-seq in control samples and FXR1 knockdown samples, and compared the gene expression profiles to explore the effect of FXR1 knockdown on gene expression. The study was performed in H358 cells. Doxycycline inducible shRNA3 (sh3) was used to knockdown FXR1. Control shRNA (ctrl) samples were used to get rid of the effect of Doxycycline treatment. Both the Doxycycline treament for 3 days (D3) and 5 days (D5) samples were collected. Each sample has three repeats (rep 1, rep 2, and rep 3). The mRNA profiles were generated by deep sequencing using Illumina.Sequenced reads were trimmed for adaptor sequence, then mapped to hg19 whole genome using STAR v2.5.3 with parameters --bamRemoveDuplicatesType UniqueIdentical --outSAMmultNmax 1. Raw reads and Reads Per Kilobase per Megabase of library size (RPKM) were calculated using HOMER (PMID: 20513432). Differential gene expression was analyzed using R package DESeq2 using the raw reads.
2017-10-04 | GSE101754 | GEO
Project description:metagenome of clinical urine samples Raw sequence reads
Project description:Whole exome sequencing of 5 HCLc tumor-germline pairs. Genomic DNA from HCLc tumor cells and T-cells for germline was used. Whole exome enrichment was performed with either Agilent SureSelect (50Mb, samples S3G/T, S5G/T, S9G/T) or Roche Nimblegen (44.1Mb, samples S4G/T and S6G/T). The resulting exome libraries were sequenced on the Illumina HiSeq platform with paired-end 100bp reads to an average depth of 120-134x. Bam files were generated using NovoalignMPI (v3.0) to align the raw fastq files to the reference genome sequence (hg19) and picard tools (v1.34) to flag duplicate reads (optical or pcr), unmapped reads, reads mapping to more than one location, and reads failing vendor QC.
Project description:HDMYZ cells were treated with 2ug/ml ActD for 0, 4 and 12 hours. Small RNAs of 15-40 bases were gel-purified from 10 ug total RNA, and subjected to multiplex Illumina small RNA library preparation. Small RNA libraries were sequenced on a HiSeq2000 (Illumina) with 3 samples per lane. To quantify miRNA and isoform abundance, sequence reads were processed by the miRDeep2 package, with the following modifications. First, to remove adaptor sequence, we removed both the main adaptor sequence present in the sequencing reads, as well as the second most abundant adaptor variant. In addition, we did not restrict the size of small RNAs during adaptor removal. Second, we used miRBase v18 for mapping the reads. Third, for quantifying miRNA and isoform frequency, we limited reads to more or equal to 15 bases in length with zero mis-match during mapping. The number of reads that were mapped to known miRNAs was used to normalize read frequencies for each miRNA or each miRNA isoform. For quantification purposes, we only considered miRNAs or isoforms that had frequency >= 1x10e-6 in samples without ActD treatment, which correspond to ~21-30 reads in raw count. These miRNAs or isoforms were referred to as reliably quantifiable.To analyze mapping to the genome, we removed reads that mapped to miRNA precursors. The rest of the reads were then mapped to the genome with Bowtie.