Evidence map›Paper›PMID 33606266›Full record

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

Biological Perspectives of RNA-Sequencing Experimental Design.

Metsada Pasmanik-Chor

Abstract read
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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
–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

1 author.

Metsada Pasmanik-ChorBioinformatics Unit, G.S.W. Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel. metsada@tauex.tau.ac.il.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of high-throughput technologies has changed the conduct of biological experiments in the last decade. From single gene studies, research has shifted to measuring gene signatures at the transcriptome level. The dramatic decrease in the financial expenses of next generation sequencing techniques has enabled their routine implementation. However, very often, economic constraints restrict the number of samples and sequence quality. Careful planning and design may overcome this limitation, and attain the maximum information from a given experiment.Among the factors that affect the quality and quantity of data resulting from next generation sequencing experiments are sample size and the number of replicates, sequence depth and coverage, randomization, and batches. Here, we discuss the design of high-throughput experiments, while focusing on RNA-sequencing experiments. We suggest critical rules of thumb, from biological, statistical, and bioinformatics points of view, aimed to obtain a successful experiment, beyond the economic constraints.

Indexed as

Computational BiologyHigh-Throughput Nucleotide SequencingHumansResearch DesignRNASequence Analysis, RNATranscriptomeRNABatchDifferentially expressed genes (DGEs)Experiment designHigh-throughput (HT)Next generation sequencing (NGS)RNA-SeqSample sizeSample variabilitySequence coverageSequence depth

Identifiers

What OpenQuestion holds

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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.