Evidence map›Paper›PMID 27061184›Full record

ArticleHuman genetics2016

Anchored pseudo-de novo assembly of human genomes identifies extensive sequence variation from unmapped sequence reads.

Joshua J Faber-Hammond, Kim H Brown

Open access · greenAbstract read
In one paragraph

Article in Human genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 29% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Pan-genomics in the human genome era.Nature reviews. Genetics · 2020
    Review
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Joshua J Faber-HammondDepartment of Biology, Portland State University, 1719 SW 10th Ave., SRTC 246, Portland, 97207-0751, USA.
Kim H BrownDepartment of Biology, Portland State University, 1719 SW 10th Ave., SRTC 246, Portland, 97207-0751, USA. kibr2@pdx.edu.ORCID 0000-0002-2358-8578
Portland State University · US

Funding

Effects of Environmental Estrogen Exposure on Spontaneous Copy Number VariationR00ES018892 · NIEHS · PORTLAND STATE UNIVERSITY · PI BROWN, KIM H · 2012 to 2014
$736k
NIEHS NIH HHS R00 ES018892
6 · The paper itself

Abstract

The human genome reference (HGR) completion marked the genomics era beginning, yet despite its utility universal application is limited by the small number of individuals used in its development. This is highlighted by the presence of high-quality sequence reads failing to map within the HGR. Sequences failing to map generally represent 2-5 % of total reads, which may harbor regions that would enhance our understanding of population variation, evolution, and disease. Alternatively, complete de novo assemblies can be created, but these effectively ignore the groundwork of the HGR. In an effort to find a middle ground, we developed a bioinformatic pipeline that maps paired-end reads to the HGR as separate single reads, exports unmappable reads, de novo assembles these reads per individual and then combines assemblies into a secondary reference assembly used for comparative analysis. Using 45 diverse 1000 Genomes Project individuals, we identified 351,361 contigs covering 195.5 Mb of sequence unincorporated in GRCh38. 30,879 contigs are represented in multiple individuals with ~40 % showing high sequence complexity. Genomic coordinates were generated for 99.9 %, with 52.5 % exhibiting high-quality mapping scores. Comparative genomic analyses with archaic humans and primates revealed significant sequence alignments and comparisons with model organism RefSeq gene datasets identified novel human genes. If incorporated, these sequences will expand the HGR, but more importantly our data highlight that with this method low coverage (~10-20×) next-generation sequencing can still be used to identify novel unmapped sequences to explore biological functions contributing to human phenotypic variation, disease and functionality for personal genomic medicine.

Indexed as

GenomicsGenetic VariationGenome, HumanHigh-Throughput Nucleotide SequencingHumansSequence AlignmentSequence Analysis, DNA

Identifiers

PMID27061184
PMCPMC4899208
OpenAlexW2333116941

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.