Genome-wide hairpins datasets of animals and plants for novel miRNA prediction

Bugnon, Leandro A.; Yones, Cristian Ariel; Raad, Jonathan; Milone, Diego H.; Stegmayer, Georgina

doi:10.1016/j.dib.2019.104209

Cited by 6 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If a too-long window length is used, many hairpins can be captured inside the same sequence, thus structural features become more complex and much more difficult to recognize by the classifier. These issues were discussed properly in previous works ( Bugnon et al, 2019 , Yones et al, 2015 ). Thus, to prevent these adverse influences and to ensure that no important sequences are lost nor inappropriately trimmed, the genome is cut into overlapped segments longer than the mean length of the pre-miRNAs of interest for the species under processing (in this case, viruses).…”

Section: Identifying Novel Pre-mirnas In Sars-cov-2mentioning

confidence: 79%

“…The positive labeled samples (known pre-miRNAs) were downloaded from miRBase v22, retrieving 569 pre-miRNAs of viruses. A total of 73 structural features from the folded sequences of the virus genome and the well-known viruses pre-miRNAs were extracted with miRNAfe ( Yones et al, 2015 ) as in Bugnon et al (2019) , and normalized with z-score. The included features are: length of the sequence, MFE, cumulative size of internal loops found in the secondary structure, number of loops, absolute and relative GC content, among many others (detailed information in Supplementary Material).…”

Section: Data Preparation and Performance Measuresmentioning

confidence: 99%

“…In the particular problem of pre-miRNAs prediction, the big challengue here is that there are only tens or hundreds of well-known pre-miRNAs (the positive class), versus millions of unknown (unlabeled) sequences across the rest of the genome, most of which are really negative class albeit including yet unknown hidden pre-miRNAs. For example, the Anopheles gambiae genome has only 66 well-known pre-miRNAs, but more than 4 million hairpin-like sequences, thus giving an imbalance of 1:60,000 ( Bugnon et al, 2019 ). In the case of viruses, for example, the value of imbalance ranges from 1:30 approximately in the bovine leukemia virus, 1:130 in the Epstein-Barr virus, and up to 1:400 in the Herpes virus of turkeys, which has only 8 known pre-miRNAs and a genome of 159 kb.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning for the discovery of new pre-miRNAs: Helping the fight against COVID-19

Bugnon¹,

Raad²,

Merino³

et al. 2021

Machine Learning with Applications

Self Cite

View full text Add to dashboard Cite

The Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) has been recently found responsible for the pandemic outbreak of a novel coronavirus disease (COVID-19). In this work, a novel approach based on deep learning is proposed for identifying precursors of small active RNA molecules named microRNA (miRNA) in the genome of the novel coronavirus. Viral miRNA-like molecules have shown to modulate the host transcriptome during the infection progression, thus their identification is crucial for helping the diagnosis or medical treatment of the disease. The existence of the mature miRNAs derived from computationally predicted miRNA precursors (pre-miRNAs) in the novel coronavirus was validated with small RNA-seq data from SARS-CoV-2-infected human cells. The results demonstrate that computational models can provide accurate and useful predictions of pre-miRNAs in the SARS-CoV-2 genome, underscoring the relevance of machine learning in the response to a global sanitary emergency. Moreover, the interpretability of our model shed light on the molecular mechanisms underlying the viral infection, thus contributing to the fight against the COVID-19 pandemic and the fast development of new treatments. Our study shows how recent advances in machine learning can be used, effectively, in response to public health emergencies. The approach developed in this work could be of great help in future similar emergencies to accelerate the understanding of the singularities of any viral agent and for the development of novel therapies. Data and source code available at : https://sourceforge.net/projects/sourcesinc/files/aicovid/ .

show abstract

Section: Identifying Novel Pre-mirnas In Sars-cov-2mentioning

confidence: 79%

Section: Data Preparation and Performance Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for the discovery of new pre-miRNAs: Helping the fight against COVID-19

Bugnon¹,

Raad²,

Merino³

et al. 2021

Machine Learning with Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…The major issue with using machine learning to detect pre-miRNAs is that the number of well-known pre-miRNAs is typically few in comparison to the hundreds of thousands of candidate sequences in a genome, making this a high-class imbalanced classification challenge [ 17 ]. H. sapiens genome is an example that has 1710 well-known pre-miRNAs but over 400 million hairpin-like sequences resulting in a 1:28128 imbalance [ 18 ]. ML algorithms are generally representing with balanced data sets but in a supervised classifier, imbalanced data tend to produce a model biased towards the majority class, with low performance in the minority one yielding false positives [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Robust and efficient COVID-19 detection techniques: A machine learning approach

et al. 2022

View full text Add to dashboard Cite

The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandemic almost halted the global economy and is responsible for 6 million deaths with infection rates of over 524 million. With significant reservations, initially, the SARS-CoV-2 virus was suspected to be infected by and closely related to Bats. However, over the periods of learning and critical development of experimental evidence, it is found to have some similarities with several gene clusters and virus proteins identified in animal-human transmission. Despite this substantial evidence and learnings, there is limited exploration regarding the SARS-CoV-2 genome to putative microRNAs (miRNAs) in the virus life cycle. In this context, this paper presents a detection method of SARS-CoV-2 precursor-miRNAs (pre-miRNAs) that helps to identify a quick detection of specific ribonucleic acid (RNAs). The approach employs an artificial neural network and proposes a model that estimated accuracy of 98.24%. The sampling technique includes a random selection of highly unbalanced datasets for reducing class imbalance following the application of matriculation artificial neural network that includes accuracy curve, loss curve, and confusion matrix. The classical approach to machine learning is then compared with the model and its performance. The proposed approach would be beneficial in identifying the target regions of RNA and better recognising of SARS-CoV-2 genome sequence to design oligonucleotide-based drugs against the genetic structure of the virus.

show abstract

“…It is actually being used in most recent prediction models (Yones et al, 2017;Acar et al, 2018). Furthermore, it has been already used successfully for building 6 public genome-wide datasets Bugnon et al (2019). HextractoR helps to standardize and simplify the stem-loop extraction stage, making future prediction methods easier to use and their experiments fully reproducible.…”

Section: Introductionmentioning

confidence: 99%

HextractoR: an R package for automatic extraction of hairpins from genome-wide data

Yones¹,

Macchiaroli

Kamenetzky

et al. 2020

Preprint

View full text Add to dashboard Cite

Extracting stem-loop sequences (hairpins) from genome-wide data is very important nowadays for some data mining tasks in bioinformatics. The genome preprocessing is very important because it has a strong influence on the later steps and the final results. For example, for novel miRNA prediction, all well-known hairpins must be properly located. Although there are some scripts that can be adapted and put together to achieve this task, they are outdated, none of them guarantees finding correspondence to well-known structures in the genome under analysis, and they do not take advantage of the latest advances in secondary structure prediction. We present here an R package for automatic extraction of hairpins from genome-wide data (HextractorR). HextractoR makes an exhaustive and smart analysis of the genome in order to obtain a very good set of short sequences for further processing. Moreover, genomes can be processed in parallel and with low memory requirements. Results obtained showed that HextractoR has effectively outperformed other methods.HextractoR it is freely available at CRAN and Sourceforge.

show abstract

Genome-wide hairpins datasets of animals and plants for novel miRNA prediction

Cited by 6 publications

References 22 publications

Deep Learning for the discovery of new pre-miRNAs: Helping the fight against COVID-19

Deep Learning for the discovery of new pre-miRNAs: Helping the fight against COVID-19

Robust and efficient COVID-19 detection techniques: A machine learning approach

HextractoR: an R package for automatic extraction of hairpins from genome-wide data

Contact Info

Product

Resources

About