Elena Rivas

6.1k total citations · 2 hit papers
38 papers, 3.6k citations indexed

About

Elena Rivas is a scholar working on Molecular Biology, Nuclear and High Energy Physics and Cancer Research. According to data from OpenAlex, Elena Rivas has authored 38 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 12 papers in Nuclear and High Energy Physics and 4 papers in Cancer Research. Recurrent topics in Elena Rivas's work include RNA and protein synthesis mechanisms (22 papers), Genomics and Phylogenetic Studies (17 papers) and RNA modifications and cancer (13 papers). Elena Rivas is often cited by papers focused on RNA and protein synthesis mechanisms (22 papers), Genomics and Phylogenetic Studies (17 papers) and RNA modifications and cancer (13 papers). Elena Rivas collaborates with scholars based in United States, Spain and France. Elena Rivas's co-authors include Sean R. Eddy, Alex Bateman, Eric P. Nawrocki, Anton I. Petrov, Ioanna Kalvari, Joanna Argasinska, ROBERT FINN, Jody Clements, Thomas A. Jones and Robert J. Klein and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Journal of Molecular Biology.

In The Last Decade

Elena Rivas

35 papers receiving 3.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families

2017 639 citations Ioanna Kalvari, Joanna Argasinska et al. Nucleic Acids Research profile →
Rfam 14: expanded coverage of metagenomic, viral and microRNA families

2020 584 citations Ioanna Kalvari, Eric P. Nawrocki et al. Nucleic Acids Research profile →

Peers

Elena Rivas

MB

PS

ECOLO

GENET

CR

Yuh Shiwa Japan

Avigdor Eldar Israel

Paolo Ribeca United Kingdom

Christian Rausch Germany

T. Lund Norway

Fiona McCarthy United States

Hakim Tafer Austria

Zoltán Gáspári Hungary

Christian Höner zu Siederdissen Austria

Hugh E. Olsen United States

Yuh Shiwa Japan

Elena Rivas

1.2k ×0.4

MB

531 ×1.1

PS

320 ×0.7

ECOLO

425 ×1.0

GENET

124 ×0.3

CR

Citations per year, relative to Elena Rivas Elena Rivas (= 1×) peers Yuh Shiwa

Countries citing papers authored by Elena Rivas

Since Specialization

Citations

This map shows the geographic impact of Elena Rivas's research. 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 Elena Rivas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Elena Rivas more than expected).

Fields of papers citing papers by Elena Rivas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Elena Rivas. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Elena Rivas. The network helps show where Elena Rivas may publish in the future.

Co-authorship network of co-authors of Elena Rivas

This figure shows the co-authorship network connecting the top 25 collaborators of Elena Rivas. A scholar is included among the top collaborators of Elena Rivas based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Elena Rivas. Elena Rivas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Magnus, Marcin, et al.. (2025). RNAhub—an automated pipeline to search and align RNA homologs with secondary structure assessment. Nucleic Acids Research. 53(W1). W496–W502.

2.

Rivas, Elena, et al.. (2025). All-at-once RNA folding with 3D motif prediction framed by evolutionary information. Nature Methods. 22(10). 2094–2106.

3.

Ontiveros‐Palacios, Nancy, Emma J. Cooke, Eric P. Nawrocki, et al.. (2024). Rfam 15: RNA families database in 2025. Nucleic Acids Research. 53(D1). D258–D267. 30 indexed citations

4.

Ward, Max, et al.. (2023). Fitness functions for RNA structure design. Nucleic Acids Research. 51(7). e40–e40. 13 indexed citations

5.

Rivas, Elena. (2023). RNA covariation at helix-level resolution for the identification of evolutionarily conserved RNA structure. PLoS Computational Biology. 19(7). e1011262–e1011262. 6 indexed citations

6.

Jones, Thomas A., et al.. (2021). Discovery of 17 conserved structural RNAs in fungi. Nucleic Acids Research. 49(11). 6128–6143. 12 indexed citations

7.

Rivas, Elena. (2021). Evolutionary conservation of RNA sequence and structure. Wiley Interdisciplinary Reviews - RNA. 12(5). e1649–e1649. 34 indexed citations

8.

Rivas, Elena. (2020). RNA structure prediction using positive and negative evolutionary information. PLoS Computational Biology. 16(10). e1008387–e1008387. 49 indexed citations

9.

Rivas, Elena, Jody Clements, & Sean R. Eddy. (2020). Estimating the power of sequence covariation for detecting conserved RNA structure. Bioinformatics. 36(10). 3072–3076. 67 indexed citations

10.

Kalvari, Ioanna, Eric P. Nawrocki, Nancy Ontiveros‐Palacios, et al.. (2020). Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Research. 49(D1). D192–D200. 584 indexed citations breakdown →

11.

Kalvari, Ioanna, Joanna Argasinska, Natalia Quinones‐Olvera, et al.. (2017). Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families. Nucleic Acids Research. 46(D1). D335–D342. 639 indexed citations breakdown →

12.

Rivas, Elena, Jody Clements, & Sean R. Eddy. (2016). A statistical test for conserved RNA structure shows lack of evidence for structure in lncRNAs. Nature Methods. 14(1). 45–48. 244 indexed citations

13.

Rivas, Elena & Sean R. Eddy. (2015). Parameterizing sequence alignment with an explicit evolutionary model. BMC Bioinformatics. 16(1). 406–406. 16 indexed citations

14.

Rivas, Elena. (2013). The four ingredients of single-sequence RNA secondary structure prediction. A unifying perspective. RNA Biology. 10(7). 1185–1196. 38 indexed citations

15.

Rivas, Elena, R. Lang, & Sean R. Eddy. (2011). A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more. RNA. 18(2). 193–212. 88 indexed citations

16.

Val, Coral del, Elena Rivas, Omar Torres‐Quesada, Nicolás Toro, & José I. Jiménez‐Zurdo. (2007). Identification of differentially expressed small non‐coding RNAs in the legume endosymbiont Sinorhizobium meliloti by comparative genomics. Molecular Microbiology. 66(5). 1080–1091. 88 indexed citations

17.

Rivas, Elena. (2005). Evolutionary models for insertions and deletions in a probabilistic modeling framework. BMC Bioinformatics. 6(1). 63–63. 45 indexed citations

18.

Rivas, Elena & Sean R. Eddy. (2000). The language of RNA: a formal grammar that includes pseudoknots. Bioinformatics. 16(4). 334–340. 110 indexed citations

19.

Rivas, Elena & Sean R. Eddy. (2000). Secondary structure alone is generally not statistically significant for the detection of noncoding RNAs. Bioinformatics. 16(7). 583–605. 207 indexed citations

20.

Rivas, Elena & Sean R. Eddy. (1999). A dynamic programming algorithm for RNA structure prediction including pseudoknots 1 1Edited by I. Tinoco. Journal of Molecular Biology. 285(5). 2053–2068. 465 indexed citations

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.

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