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Sample GSM6355110

Query DataSets for GSM6355110

Status

Public on Jul 26, 2022

Title

D.magna whole body, Lab Water-239 ng/L pyriproxyfen-Summer-Rep3 5_S31

Sample type

SRA

Source name

D.magna whole body

Organism

Daphnia magna

Characteristics

tissue: D.magna whole body
treatment: Lab Water-239 ng/L pyriproxyfen-Summer

Treatment protocol

At the end of the 96‐h exposure period, to obtain sufficient RNA quantity for sequencing, the 30 treated (less 0%–7% mortality) neonates were randomly divided into five groups of five individuals, then stored in 100 µl of RNAlater (catalog no. R0901; Sigma‐Aldrich) at −80 °C until RNA extraction. RNA was extracted by sonication (30 s)

Growth protocol

Live D. magna (purchased from Carolina Biological Supply) were placed into aerated St. Cloud State University well water and maintained as an experimental culture under a 16:8‐h light:dark photoperiod. All test water conditions were within Organisation for Economic Co‐operation and Development (OECD) Test No. 211 (OECD, 2012) guidelines.

Extracted molecule

total RNA

Extraction protocol

Total RNA extration via PureLink RNA Mini Kit (catalog no. 12183018 A; Thermo Fisher Scientific)
Strand‐specific messenger RNA libraries were created for each total RNA sample of pooled daphnia (n = 5 individual neonates per replicate sample; five groups per treatment).

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina HiSeq 2500

Description

D.magna whole body, Lab Water-239 ng/L pyriproxyfen-Summer-Rep3
Organism Source: Carolina Biological Supply, 2014

Data processing

Raw sequences were evaluated via FastQC (Andrews, 2010) to determine the baseline quality of the sequencing results. To improve these overall statistics, reads were trimmed to remove Illumina‐specific adapter sequences and low‐quality sequences or sequence tails using the Trimmomatic software (Bolger et al., 2014). FastQC was run again to verify that only high‐quality sequences were retained.
High‐quality sequences were then mapped to D. magna reference transcripts using Kallisto (Bray et al., 2016), a kmer‐based mapper. Kallisto produces a matrix of read counts with transcripts along one axis and samples along the other. To produce a data set for direct differential expression analysis, each cell of this matrix was rounded to the nearest integer value to be compatible with the DESeq. 2 algorithm (Love et al., 2014; an R Package available through Bioconductor). To produce a data set to identify minimal gene signatures separating conditions, each read count was divided by the total reads counted for that sample, producing a fractional matrix. All genes that showed no variance in any one data set were removed from analysis for all data sets to ensure cross‐data set comparability. Initial comparisons indicated a modest batch effect between the wetland and storm‐water samples. A linear model was therefore used to identify and remove batch variance that may have been a result of employing two different genetic backgrounds for each study.
Assembly: RNA-seq reads from this study were aligned to a transcriptome created from the RNA sequencing of 12 Daphnia magna colonies under different stress conditions. Orsini, L., D. Gilbert, R. Podicheti, M. Jansen, J. B. Brown, O. S. Solari, K. I. Spanier, J. K. Colbourne, D. B. Rusch, and E. Decaestecker. 2016. Daphnia magna transcriptome by RNA-Seq across 12 environmental stressors. Scientific Data 3:1-16.
Supplementary files format and content: Kallisto.counts is a matrix of raw counts. Kallisto produces a matrix of read counts with transcripts along one axis and samples along the other. Matrix of read counts with transcripts along one axis and samples along the other.
Supplementary files format and content: Kallisto.tpm is a matrix processed as described herein. Kallisto produces a matrix of read counts with transcripts along one axis and samples along the other. Matrix of read counts with transcripts along one axis and samples along the other. Each cell of this matrix was rounded to the nearest integer value to be compatible with the DESeq2 algorighim. Each read count was then divided by the total reads counted for that sample, producing a fractional matrix. All genes that showed no variance in any one dataset were removed from analysis for all datasets to ensure cross-dataset comparability.

Submission date

Jul 19, 2022

Last update date

Jul 26, 2022

Contact name

Mark Jankowski

E-mail(s)

jankowski.mark@epa.gov

Phone

2065531476

Organization name

USEPA