Evidence map›Paper›PMID 35876555›Full record

ArticleCurrent protocols2022

Getting Started with LINCS Datasets and Tools.

Zhuorui Xie, Eryk Kropiwnicki, Megan L Wojciechowicz, Kathleen M Jagodnik, Ingrid Shu, Allison Bailey, Daniel J B Clarke, Minji Jeon, John Erol Evangelista, Maxim V Kuleshov and 5 more

Open access · greenAbstract read
In one paragraph

Article in Current protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
8.2field-weighted citation impact, top 3% 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

18 citing papers in PubMed, 33 citations in OpenAlex.

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  14. Transcriptome Complexity Disentangled: A Regulatory Molecules Approach.International journal of molecular sciences · 2025
    Article
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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

15 authors at 2 institutions in 1 country.

Zhuorui XieDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Eryk KropiwnickiDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Megan L WojciechowiczDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Kathleen M JagodnikDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Ingrid ShuDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Allison BaileyDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Daniel J B ClarkeDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Minji JeonDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
John Erol EvangelistaDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Maxim V KuleshovDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Alexander LachmannDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Abhijna A ParigiSchool of Veterinary Medicine, University of California Davis, Davis, California.
Jose M SanchezSchool of Veterinary Medicine, University of California Davis, Davis, California.
Sherry L JenkinsDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Avi Ma'ayanDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Icahn School of Medicine at Mount Sinai · USUniversity of California, Davis · US

Funding

University of Maryland NIH Data Commons Facilitation CenterOT3OD025459 · OD · UNIVERSITY OF MARYLAND BALTIMORE · PI WHITE, OWEN R · 2017 to 2023
$23.5M
Data Coordination and Integration Center for LINCS-BD2KU54HL127624 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI MA'AYAN, AVI, SCHURER, STEPHAN C · 2014 to 2019
$23.5M
TRAINING PROGRAM IN MOLECULAR AND CELLULAR CARDIOLOGYT32HL007824 · NHLBI · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI GELB, BRUCE D · 1995 to 2022
$8.6M
The LINCS DCIC Engagement Plan with the CFDEOT2OD030160 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI MA'AYAN, AVI · 2020 to 2024
$3.4M
Knowledge Management Center for Illuminating the Druggable GenomeU24CA224260 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI MA'AYAN, AVI · 2018 to 2023
$1.5M
NCI NIH HHS U24 CA224260NHLBI NIH HHS T32 HL007824NHLBI NIH HHS U54 HL127624NIH HHS OT2 OD030160NIH HHS OT2OD030160NIH HHS OT3 OD025459NIH HHS OT3OD025459NIH HHS U54HL127624
6 · The paper itself

Abstract

The Library of Integrated Network-based Cellular Signatures (LINCS) was an NIH Common Fund program that aimed to expand our knowledge about human cellular responses to chemical, genetic, and microenvironment perturbations. Responses to perturbations were measured by transcriptomics, proteomics, cellular imaging, and other high content assays. The second phase of the LINCS program, which lasted 7 years, involved the engagement of six data and signature generation centers (DSGCs) and one data coordination and integration center (DCIC). The DSGCs and the DCIC developed several digital resources, including tools, databases, and workflows that aim to facilitate the use of the LINCS data and integrate this data with other publicly available data. The digital resources developed by the DSGCs and the DCIC can be used to gain new biological and pharmacological insights that can lead to the development of novel therapeutics. This protocol provides step-by-step instructions for processing the LINCS data into signatures, and utilizing the digital resources developed by the LINCS consortia for hypothesis generation and knowledge discovery. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Navigating L1000 tools and data in CLUE.io Basic Protocol 2: Computing signatures from the L1000 data with the CD method Basic Protocol 3: Analyzing lists of differentially expressed genes and querying them against the L1000 data with BioJupies and the Bulk RNA-seq Appyter Basic Protocol 4: Utilizing the L1000FWD resource for drug discovery Basic Protocol 5: KINOMEscan and the KINOMEscan Appyter Basic Protocol 6: LINCS P100 and GCP Proteomics Assays Basic Protocol 7: The LINCS Joint Project (LJP) Basic Protocol 8: The LINCS Data Portals and SigCom LINCS Basic Protocol 9: Creating and analyzing signatures with iLINCS.

Indexed as

Drug DiscoveryProteomicsDatabases, FactualGene LibraryHumansTranscriptomebioinformaticsdiseasedrug discoverygene sets visualizationsignature analysisweb application

Identifiers

PMID35876555
PMCPMC9326873
OpenAlexW4287378669

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.