Evidence map›Paper›PMID 37629524›Full record

ReviewLife (Basel, Switzerland)2023

The Progression in Developing Genomic Resources for Crop Improvement.

Pradeep Ruperao, Parimalan Rangan, Trushar Shah, Vivek Thakur, Sanjay Kalia, Sean Mayes, Abhishek Rathore

Abstract readReview
In one paragraph

Review in Life (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

7 authors.

Pradeep RuperaoCenter of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India.ORCID 0000-0002-5222-9988
Parimalan RanganICAR-National Bureau of Plant Genetic Resources, PUSA Campus, New Delhi 110012, India.ORCID 0000-0003-1660-8072
Trushar ShahInternational Institute of Tropical Agriculture (IITA), Nairobi 30709-00100, Kenya.ORCID 0000-0002-0091-7981
Vivek ThakurDepartment of Systems & Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad 500046, India.
Sanjay KaliaDepartment of Biotechnology, Ministry of Science and Technology, Government of India, New Delhi 110003, India.
Sean MayesCenter of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India.ORCID 0000-0003-4770-3662
Abhishek RathoreExcellence in Breeding, International Maize and Wheat Improvement Center (CIMMYT), Hyderabad 502324, India.

Funding

Bill & Melinda Gates Foundation INV-037010
6 · The paper itself

Abstract

Sequencing technologies have rapidly evolved over the past two decades, and new technologies are being continually developed and commercialized. The emerging sequencing technologies target generating more data with fewer inputs and at lower costs. This has also translated to an increase in the number and type of corresponding applications in genomics besides enhanced computational capacities (both hardware and software). Alongside the evolving DNA sequencing landscape, bioinformatics research teams have also evolved to accommodate the increasingly demanding techniques used to combine and interpret data, leading to many researchers moving from the lab to the computer. The rich history of DNA sequencing has paved the way for new insights and the development of new analysis methods. Understanding and learning from past technologies can help with the progress of future applications. This review focuses on the evolution of sequencing technologies, their significant enabling role in generating plant genome assemblies and downstream applications, and the parallel development of bioinformatics tools and skills, filling the gap in data analysis techniques.

Indexed as

artificial intelligenceassembliesbig databioinformatics toolsdatabasesmachine learningpan-genomesplant genomessequencing technologies

Identifiers

PMID37629524
PMCPMC10455509

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.