ArticleGenomics2014
Large scale comparison of non-human sequences in human sequencing data.
Article in Genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 32 citations in OpenAlex.
- hgtseq: A Standard Pipeline to Study Horizontal Gene Transfer.International journal of molecular sciences · 2022Article
- Whole-Genome Sequencing Reveals Age-Specific Changes in the Human Blood Microbiota.Journal of personalized medicine · 2022Article
- Bovine Meat and Milk Factors (BMMFs): Their Proposed Role in Common Human Cancers and Type 2 Diabetes Mellitus.Cancers · 2021Review
- Bacterial Diversity Correlates with Overall Survival in Cancers of the Head and Neck, Liver, and Stomach.Molecules (Basel, Switzerland) · 2021Article
- Harmonization of whole-genome sequencing for outbreak surveillance ofMicrobial genomics · 2021Article
- Article
- Unmapped exome reads implicate a role for Anelloviridae in childhood HIV-1 long-term non-progression.NPJ genomic medicine · 2021Article
- Sequencing facility and DNA source associated patterns of virus-mappable reads in whole-genome sequencing data.Genomics · 2021Article
- Tissue-associated microbial detection in cancer using human sequencing data.BMC bioinformatics · 2020Review
- What human sperm RNA-Seq tells us about the microbiome.Journal of assisted reproduction and genetics · 2020Article
- The landscape of bacterial presence in tumor and adjacent normal tissue across 9 major cancer types using TCGA exome sequencing.Computational and structural biotechnology journal · 2020Article
- A virome-wide clonal integration analysis platform for discovering cancer viral etiology.Genome research · 2019Article
- From trash to treasure: detecting unexpected contamination in unmapped NGS data.BMC bioinformatics · 2019Article
- Exploring the unmapped DNA and RNA reads in a songbird genome.BMC genomics · 2019Article
- Article
- Using reference-free compressed data structures to analyze sequencing reads from thousands of human genomes.Genome research · 2017Article
- Lessons for livestock genomics from genome and transcriptome sequencing in cattle and other mammals.Genetics, selection, evolution : GSE · 2016Article
- Genomic leftovers: identifying novel microsatellites, over-represented motifs and functional elements in the human genome.Scientific reports · 2016Article
- Small RNA-Based Antiviral Defense in the Phytopathogenic Fungus Colletotrichum higginsianum.PLoS pathogens · 2016Article
- What's in your next-generation sequence data? An exploration of unmapped DNA and RNA sequence reads from the bovine reference individual.BMC genomics · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Several studies have demonstrated that unmapped reads in next generation sequencing data could be used to identify infectious agents or structural variants, but there has been no intensive effort to analyze and classify all non-human sequences found in individual large data sets. To identify commonality in non-human sequences by infectious agents and putative contamination events, we analyzed non-human sequences in 150 genomic sequencing data files from the 1000 Genomes Project and observed that 0.13% of reads on average showed similarities to non-human genomes. We compared results among different sample groups divided based on ethnicities, sequencing centers and enrichment methods (whole genome sequencing vs. exome sequencing) and found that sequencing centers had specific signatures of contaminating genomes as 'time stamps'. We also observed many unmapped reads that falsely indicated contamination because of the high similarity of human sequences to sequences in non-human genome assemblies such as mouse and Nicotiana.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.