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RNA-Seq analysis facilitates quantitative analysis to identify DEGs in lung cancer cell lines regulated by S100A7A introduction in comparison with Cl1-0 control and S100A7A knockdown in comparison with CL1-5 control.

ABSTRACT: Methods: The cDNA libraries from 3 pooled samples of cultured cells for each group were sequenced to generate RNA profiles using Illumina Miseq platform Results: The sequencing runs yielded a total of 95.01 M reads with an average length of 73-74 bp. The high-throughput sequencing performed for liver samples with different treatments showed similar numbers of yielded reads ranged from 5.57 to 5.74 M and the same average length. The Strand NGS software (version 2.1) was used using default parameters for pre-alignment and post-alignment quality control analysis and 100% of the raw reads remained in the dataset. Of these, 19.02 M reads (84%) were mapped into contigs of the rat genome (rn5) and identified 31457 transcripts in liver samples.

ORGANISM(S): Homo sapiens

PROVIDER: GSE86344 | GEO | 2017/01/31

SECONDARY ACCESSION(S): PRJNA341495

REPOSITORIES: GEO

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Project description:Small RNA sequencing has been performed using eight commonly used breast cancer cell lines by Illumina as part of the Illumina iDEA Challange 2011. Total RNA of the cell lines was isolated and the sequencing library was prepared using Illuminas Small RNA v1.5 library prep protocol. This data was analyzed with Flicker, an add-on to the Illumina Genome Analyzer Pipeline software that allows for the preliminary analysis of small RNA runs on the GA. Flicker was used in this case for: * Trimming: Attempts to trim the adaptor sequence from small RNA reads Trimming is accomplished by aligning the adaptor with each read, both internally (the adaptor is fully contained within the read) and as a suffix (the adaptor overlaps the 3' end of the read). A similarity matrix is used to generate the matching score, taking into account both substitutions and indels. The alignment with the best matching score is returned, and that best alignment is trimmed from the read. A trimming threshold may be specified, and the adaptor is not trimmed unless the best score exceeds the threshold. * Aligning with Iterated ELAND: Attempts to aligns trimmed reads to targets (squashed genomes or databases such as miRBase) using the ELAND short read aligner. Each read is aligned by ELAND with specified alignment targets. ELAND was designed to align reads of a uniform length, and the trimmed reads from a small RNA run are several different lengths. We currently address this by running ELAND for reads of each length: reads are binned by length, and each bin of a given length is aligned against a specified target. Once all bins are aligned against all targets (in this case, gDNA and miRBase), the results are recombined into a single file with all the annotation information. ===== Key to Experiments HCT ID, Lane, Sample ID, Cell Line, ER +/- Status, Reads HCT20061, Lane 1, HS378, MCF7, +, 14,555,390 HCT20062, Lane 2, HS379, MDA-MB-231, -, 15,147,037 HCT20063, Lane 3, HS381, T47D, +, 14,701,254 HCT20064, Lane 4, HS377, BT-20, -, 16,529,823 HCT20065, Lane 5, HS375, BT-474, +, 16,443,361 HCT20066, Lane 6, HS380, MDA-MB-468, -, 16,746,981 HCT20067, Lane 7, HS382, ZR-75-1, +, 17,631,005 HCT20068, Lane 8, HS376, MCF10A, normal, 19,155,607

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RNA-Seq analysis facilitates quantitative analysis to identify DEGs in lung cancer cell lines regulated by S100A7A introduction in comparison with Cl1-0 control and S100A7A knockdown in comparison with CL1-5 control.

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