Evidence map›Paper›PMID 33835446›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2021

In Silico Analysis of Micro-RNA Sequencing Data.

Ernesto Aparicio-Puerta, Bastian Fromm, Michael Hackenberg, Marc K Halushka

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.5field-weighted citation impact, top 42% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 3 institutions in 3 countries.

Ernesto Aparicio-PuertaDepartment of Genetics, University of Granada, Granada, Spain.
Bastian FrommDepartment of Molecular Biosciences, Stockholm University, Stockholm, Sweden.
Michael HackenbergDepartment of Genetics, University of Granada, Granada, Spain.
Marc K HalushkaDepartment of Pathology, Johns Hopkins University, Baltimore, MD, USA. mhalush1@jhmi.edu.
Universidad de Granada · ESJohns Hopkins University · USStockholm University · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput sequencing for micro-RNAs (miRNAs) to obtain expression estimates is a central method of molecular biology. Surprisingly, there are a number of different approaches to converting sequencing output into micro-RNA counts. Each has their own strengths and biases that impact on the final data that can be obtained from a sequencing run. This chapter serves to make the reader aware of the trade-offs one must consider in analyzing small RNA sequencing data. It then compares two methods, miRge2.0 and the sRNAbench and the steps utilized to output data from their tools.

Indexed as

AnimalsComputational BiologyComputer SimulationDatasets as TopicGene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansMicroRNAsPolymorphism, Single NucleotideRNA IsoformsSequence Analysis, RNASoftwareMicroRNAsRNA IsoformsAlignmentBowtieisomiRMicro-RNAmiRBaseMirGeneDBSmall RNA sequencing

Identifiers

PMID33835446
OpenAlexW3149736520

What OpenQuestion holds

Textmetadata
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.