Expression profile analysis of long non-coding RNAs in condyloma acuminatum in a Chinese female population by RNA-sequencing and bioinformatics
RNA-seq (gene expression) · human · 30 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 30 samples: 22 distinct titles, 2 of them repeatedGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- Alteration of RNA m6A methylation mediates aberrant RNA binding protein expression and alternative splicing in condyloma acuminatumPeerJ · 2024
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE172140_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA722228 and SRA study SRP314961. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 30 sample titles: 22 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM5242593 CA_1
- GSM5242594 CA_2
- GSM5242595 CA_3
- GSM5242596 CA_4
- GSM5242597 CA_5
- GSM5242598 CON_1
- GSM5242599 CON_2
- GSM5242600 CON_3
- GSM5242601 CON_4
- GSM5242602 CON_5
- GSM5242610 CA_1_input
- GSM5242611 CA_1_IP
- GSM5242612 CA_2_input
- GSM5242613 CA_2_IP
- GSM5242614 CA_3_input
- GSM5242615 CA_3_IP
- GSM5242616 CA_4_input
- GSM5242617 CA_4_IP
- GSM5242618 CA_5_input
- GSM5242619 CA_5_IP
- GSM5242620 CON_1_input
- GSM5242621 CON_1_IP
- GSM5242622 CON_2_input
- GSM5242623 CON_2_IP
- GSM5242624 CON_3_input
- GSM5242625 CON_3_IP
- GSM5242626 CON_4_input
- GSM5242627 CON_4_IP
- GSM5242628 CON_5_input
- GSM5242629 CON_5_IP
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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.