Evidence map›Paper›PMID 35819611›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Computational Analysis of HTS Data and Its Application in Plant Pathology.

Edoardo Piombo, Mukesh Dubey

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. 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
–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

0 citing papers in PubMed.

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

2 authors.

Edoardo PiomboDepartment of Forest Mycology and Plant Pathology, Uppsala Biocenter, Swedish University of Agricultural Sciences, Uppsala, Sweden. edoardo.piombo@slu.se.
Mukesh DubeyDepartment of Forest Mycology and Plant Pathology, Uppsala Biocenter, Swedish University of Agricultural Sciences, Uppsala, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput sequencing is a basic tool of biological research, and it is extensively used in plant pathology projects. Here, we describe how to handle data coming from a variety of sequencing experiments, focusing on the analysis of Illumina reads. We describe how to perform genome assembly and annotation with DNA reads, correctly analyze RNA-seq data to discover differentially expressed genes, handle amplicon sequencing data from microbial communities, and utilize small RNA sequencing data to predict miRNA sequences and their putative targets.

Indexed as

MicroRNAsPlant PathologyBase SequenceHigh-Throughput Nucleotide SequencingSequence Analysis, RNAMicroRNAsDifferential expressionFunctional annotationGenome assemblyHigh-throughput sequencingMetabarcodingmiRNASmall RNATarget predictionTranscriptomics

Identifiers

PMID35819611

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.