Project description:We used PacBio data to identify more reliable transcripts from hESC, based on which we can estimate gene/transcript abundance better from Illumina data. PacBio long reads and Illumina short reads were generated from the same hESC cell line H1. PacBio reads were error-corrected by Illumina reads to identify transcripts. rSeq is used to estimate gene/transcript abundance of the identified transcriptome.
Project description:We used PacBio data to identify more reliable transcripts from hESC, based on which we can estimate gene/transcript abundance better from Illumina data.
Project description:Full-Length cDNA transcriptome (Iso-Seq) data sequenced on the PacBio Sequel system using 2.1 chemistry. Multiplexed cDNA library of 12 samples (3 tissues x 4 strains). Tissues: root, embryo, endosperm. Strains: B73, Ki11, B73xKi11, Ki11xB73.
Project description:Rapidly increased studies by third-generation sequencing [Pacific Biosciences (Pacbio) and Oxford Nanopore Technologies (ONT)] have been used in all kinds of research areas. Among them, the plant full-length single-molecule transcriptome studies were most used by Pacbio while ONT was rarely used. Therefore, in this study, we developed ONT RNA-sequencing methods in plants. We performed a detailed evaluation of reads from Pacbio and Nanopore PCR cDNA (ONT Pc) sequencing in plants (Arabidopsis), including the characteristics of raw data and identification of transcripts. We aimed to provide a valuable reference for applications of ONT in plant transcriptome analysis.
Project description:Here, we sequenced and functionally annotated the long reads (1-2 kb) cDNAs library of an infratentorial ependymoma tumor tissue on PacBio RSII by Iso-Seq protocol using SMRT technology. 577 MB, data was generated from the brain tissues of ependymoma tumor patient, producing 1,19,313 high-quality reads assembled into 19,878 contigs using Celera assembler followed by Quiver pipelines, which produced 2952 unique protein accessions in the nr protein database and 307 KEGG pathways. Additionally, when we compared GO terms of second and third level with alternative splicing data obtained through HTA Array2.0. We identified four and twelve transcript cluster IDs in Level-2 and Level-3 scores respectively with alternative splicing index predicting mainly the major pathways of hallmarks of cancer. Out of these transcript cluster IDs only transcript cluster IDs of gene PNMT, SNN and LAMB1 showed Reads Per Kilobase of exon model per Million mapped reads (RPKM) values at gene-level expression (GE) and transcript-level (TE) track. Most importantly, brain-specific genes--PNMT, SNN and LAMB1 show their involvement in Ependymoma.
