Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

4.4k indexed citations

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This paper, published in 2016, received 4.4k indexed citations. Written by Mihaela Pertea, Daehwan Kim, Geo Pertea, Jeffrey T. Leek and Steven L. Salzberg covering the research area of Molecular Biology. It is primarily cited by scholars working on Molecular Biology (2.5k citations), Plant Science (1.5k citations) and Cancer Research (687 citations). Published in Nature Protocols.

Countries where authors are citing Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

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This map shows the geographic impact of Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown more than expected).

Fields of papers citing Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1038/nprot.2016.095.

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