Evidence map›Paper›PMID 32717667›Full record

ReviewCurrent opinion in genetics & development2020

Methods for detecting introgressed archaic sequences.

Sriram Sankararaman

Abstract readReview
In one paragraph

Review in Current opinion in genetics & development, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Introgression.Current biology : CB · 2022
    Article
  7. "Archealization" of human brain organoids.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022
    Article
  8. 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

1 author.

Sriram SankararamanDepartment of Computer Science, University of California, Los Angeles, CA 90095, United States; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, United States; Department of Computational Medicine, University of California, Los Angeles, CA 90095, United States. Electronic address: sriram@cs.ucla.edu.

Funding

Statistical Models for Dissecting Human Population Admixture and its Role in Evolution and DiseaseR35GM125055 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SANKARARAMAN, SRIRAM · 2017 to 2021
$1.6M
NIGMS NIH HHS R35 GM125055
6 · The paper itself

Abstract

Analysis of genome sequences from archaic and modern humans have revealed multiple episodes of admixture between highly-diverged population groups. Statistical methods that attempt to localize DNA segments introduced by these events offer a powerful tool to investigate recent human evolution. We review recent advances in methods for detecting introgressed sequences.

Indexed as

Evolution, MolecularGenome, HumanModels, GeneticAnimalsDNAGenetic VariationHominidaeHumansNeanderthalsDNA

Identifiers

PMID32717667
PMCPMC7484293

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.