Distinct patterns of metagenomic surveillance and respiratory microbiota between two P1 genotypes of Mycoplasma pneumoniae
RNA-seq (gene expression) · human · 174 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
- 174 samples: 174 samples, each with a distinct title — no replicated groupsGroups 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.
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 GSE274269_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 PRJNA1144209 and SRA study SRP524370. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 174 sample titles: 174 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.
- GSM8446549 2098311008707
- GSM8446550 2180951131416
- GSM8446551 2180961061598
- GSM8446552 2098311006093
- GSM8446553 2098353033439
- GSM8446554 2180944124326
- GSM8446555 1958611003943
- GSM8446556 2098311007533
- GSM8446557 1956044151019
- GSM8446558 2180941068937
- GSM8446559 2234041190545
- GSM8446560 2180932280652
- GSM8446561 2180944282736
- GSM8446562 2098311008691
- GSM8446563 2180943020902
- GSM8446564 1965611004450
- GSM8446565 1956035020423
- GSM8446566 1958611002557
- GSM8446567 1956032008790
- GSM8446568 2098311007557
- GSM8446569 2180943140099
- GSM8446570 2234032300410
- GSM8446571 2180944164070
- GSM8446572 1957544082155
- GSM8446573 2180941194858
- GSM8446574 2180932070574
- GSM8446575 2234041101527
- GSM8446576 1951611003685
- GSM8446577 2180941095131
- GSM8446578 2098311008844
- GSM8446579 2180943020582
- GSM8446580 2234014020084
- GSM8446581 1958611003028
- GSM8446582 2098311008646
- GSM8446583 1957544018253
- GSM8446584 1957532004459
- GSM8446585 1997811002509
- GSM8446586 2180942073534
- GSM8446587 1997811002417
- GSM8446588 1965611004474
+ 134 more — browse all 174 samples with per-sample file links →
- GSE316337 Multi-omics profiling reveals microbiota, metabolite, lipid, and immunological heterogeneity underlying distinct pathophysiological mechanisms of age-related endotypes in type 1 diabetes 54 samples shares microbiota, distinct
- GSE77087 Nasopharyngeal microbiota, host transcriptome and disease severity in children with respiratory syncytial virus infection 104 samples shares respiratory, microbiota
- GSE282464 Molecular landscape of respiratory infection: A large-scale, multi-centre blood transcriptome dataset 377 samples shares respiratory
- GSE156063 Upper airway gene expression differentiates COVID-19 from other acute respiratory illnesses and reveals suppression of innate immune responses by SARS-CoV-2 234 samples shares respiratory
- GSE309130 Dual-transcriptomic analysis of human nasal transcriptome and microbiome reveals host-bacteria interactions associated with symptomatic respiratory infection [RNA-Seq] 149 samples shares respiratory
- GSE311749 Upper Airway Gene Expression in Hospitalized Children with Rhinovirus-induced Respiratory Illnesses 86 samples shares respiratory
- GSE174799 Differences in vaginal microbiota, host transcriptome and proteins in women with bacterial vaginosis are associated with metronidazole treatment response 73 samples shares microbiota
- GSE231572 Development and validation of a microbiota signature to predict prognosis in nasopharyngeal carcinoma 64 samples shares microbiota
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.