Pub Date : 2024-02-22eCollection Date: 2023-01-01DOI: 10.3389/fbinf.2023.1285828
Yanlin Zhang, Christopher J F Cameron, Mathieu Blanchette
Hi-C is one of the most widely used approaches to study three-dimensional genome conformations. Contacts captured by a Hi-C experiment are represented in a contact frequency matrix. Due to the limited sequencing depth and other factors, Hi-C contact frequency matrices are only approximations of the true interaction frequencies and are further reported without any quantification of uncertainty. Hence, downstream analyses based on Hi-C contact maps (e.g., TAD and loop annotation) are themselves point estimations. Here, we present the Hi-C interaction frequency sampler (HiCSampler) that reliably infers the posterior distribution of the interaction frequency for a given Hi-C contact map by exploiting dependencies between neighboring loci. Posterior predictive checks demonstrate that HiCSampler can infer highly predictive chromosomal interaction frequency. Summary statistics calculated by HiCSampler provide a measurement of the uncertainty for Hi-C experiments, and samples inferred by HiCSampler are ready for use by most downstream analysis tools off the shelf and permit uncertainty measurements in these analyses without modifications.
{"title":"Posterior inference of Hi-C contact frequency through sampling.","authors":"Yanlin Zhang, Christopher J F Cameron, Mathieu Blanchette","doi":"10.3389/fbinf.2023.1285828","DOIUrl":"10.3389/fbinf.2023.1285828","url":null,"abstract":"<p><p>Hi-C is one of the most widely used approaches to study three-dimensional genome conformations. Contacts captured by a Hi-C experiment are represented in a contact frequency matrix. Due to the limited sequencing depth and other factors, Hi-C contact frequency matrices are only approximations of the true interaction frequencies and are further reported without any quantification of uncertainty. Hence, downstream analyses based on Hi-C contact maps (e.g., TAD and loop annotation) are themselves point estimations. Here, we present the Hi-C interaction frequency sampler (HiCSampler) that reliably infers the posterior distribution of the interaction frequency for a given Hi-C contact map by exploiting dependencies between neighboring loci. Posterior predictive checks demonstrate that HiCSampler can infer highly predictive chromosomal interaction frequency. Summary statistics calculated by HiCSampler provide a measurement of the uncertainty for Hi-C experiments, and samples inferred by HiCSampler are ready for use by most downstream analysis tools off the shelf and permit uncertainty measurements in these analyses without modifications.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10919286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140061442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-31eCollection Date: 2024-01-01DOI: 10.3389/fbinf.2024.1347168
A L Swan, A Broadbent, P Singh Gaur, A Mishra, K Gurwitz, A Mithani, S L Morgan, G Malhotra, C Brooksbank
EMBL-EBI provides a broad range of training in data-driven life sciences. To improve awareness and access to training course listings and to make digital learning materials findable and simple to use, the EMBL-EBI Training website, www.ebi.ac.uk/training, was redesigned and restructured. To provide a framework for the redesign of the website, the FAIR (findable, accessible, interoperable, reusable) principles were applied to both the listings of live training courses and the presentation of on-demand training content. Each of the FAIR principles guided decisions on the choice of technology used to develop the website, including the details provided about training and the way in which training was presented. Since its release the openly accessible website has been accessed by an average of 58,492 users a month. There have also been over 12,000 unique users creating accounts since the functionality was added in March 2022, allowing these users to track their learning and record completion of training. Development of the website was completed using the Agile Scrum project management methodology and a focus on user experience. This framework continues to be used now that the website is live for the maintenance and improvement of the website, as feedback continues to be collected and further ways to make training FAIR are identified. Here, we describe the process of making EMBL-EBI's training FAIR through the development of a new website and our experience of implementing Agile Scrum.
{"title":"Making bioinformatics training FAIR: the EMBL-EBI training portal.","authors":"A L Swan, A Broadbent, P Singh Gaur, A Mishra, K Gurwitz, A Mithani, S L Morgan, G Malhotra, C Brooksbank","doi":"10.3389/fbinf.2024.1347168","DOIUrl":"10.3389/fbinf.2024.1347168","url":null,"abstract":"<p><p>EMBL-EBI provides a broad range of training in data-driven life sciences. To improve awareness and access to training course listings and to make digital learning materials findable and simple to use, the EMBL-EBI Training website, www.ebi.ac.uk/training, was redesigned and restructured. To provide a framework for the redesign of the website, the FAIR (findable, accessible, interoperable, reusable) principles were applied to both the listings of live training courses and the presentation of on-demand training content. Each of the FAIR principles guided decisions on the choice of technology used to develop the website, including the details provided about training and the way in which training was presented. Since its release the openly accessible website has been accessed by an average of 58,492 users a month. There have also been over 12,000 unique users creating accounts since the functionality was added in March 2022, allowing these users to track their learning and record completion of training. Development of the website was completed using the Agile Scrum project management methodology and a focus on user experience. This framework continues to be used now that the website is live for the maintenance and improvement of the website, as feedback continues to be collected and further ways to make training FAIR are identified. Here, we describe the process of making EMBL-EBI's training FAIR through the development of a new website and our experience of implementing Agile Scrum.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10866141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139736872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-31eCollection Date: 2024-01-01DOI: 10.3389/fbinf.2024.1293412
Aimer Gutierrez-Diaz, Steve Hoffmann, Juan Carlos Gallego-Gómez, Clara Isabel Bermudez-Santana
In recent years, a population of small RNA fragments derived from non-coding RNAs (sfd-RNAs) has gained significant interest due to its functional and structural resemblance to miRNAs, adding another level of complexity to our comprehension of small-RNA-mediated gene regulation. Despite this, scientists need more tools to test the differential expression of sfd-RNAs since the current methods to detect miRNAs may not be directly applied to them. The primary reasons are the lack of accurate small RNA and ncRNA annotation, the multi-mapping read (MMR) placement, and the multicopy nature of ncRNAs in the human genome. To solve these issues, a methodology that allows the detection of differentially expressed sfd-RNAs, including canonical miRNAs, by using an integrated copy-number-corrected ncRNA annotation was implemented. This approach was coupled with sixteen different computational strategies composed of combinations of four aligners and four normalization methods to provide a rank-order of prediction for each differentially expressed sfd-RNA. By systematically addressing the three main problems, we could detect differentially expressed miRNAs and sfd-RNAs in dengue virus-infected human dermal microvascular endothelial cells. Although more biological evaluations are required, two molecular targets of the hsa-mir-103a and hsa-mir-494 (CDK5 and PI3/AKT) appear relevant for dengue virus (DENV) infections. Here, we performed a comprehensive annotation and differential expression analysis, which can be applied in other studies addressing the role of small fragment RNA populations derived from ncRNAs in virus infection.
{"title":"Systematic computational hunting for small RNAs derived from ncRNAs during dengue virus infection in endothelial HMEC-1 cells.","authors":"Aimer Gutierrez-Diaz, Steve Hoffmann, Juan Carlos Gallego-Gómez, Clara Isabel Bermudez-Santana","doi":"10.3389/fbinf.2024.1293412","DOIUrl":"10.3389/fbinf.2024.1293412","url":null,"abstract":"<p><p>In recent years, a population of small RNA fragments derived from non-coding RNAs (sfd-RNAs) has gained significant interest due to its functional and structural resemblance to miRNAs, adding another level of complexity to our comprehension of small-RNA-mediated gene regulation. Despite this, scientists need more tools to test the differential expression of sfd-RNAs since the current methods to detect miRNAs may not be directly applied to them. The primary reasons are the lack of accurate small RNA and ncRNA annotation, the multi-mapping read (MMR) placement, and the multicopy nature of ncRNAs in the human genome. To solve these issues, a methodology that allows the detection of differentially expressed sfd-RNAs, including canonical miRNAs, by using an integrated copy-number-corrected ncRNA annotation was implemented. This approach was coupled with sixteen different computational strategies composed of combinations of four aligners and four normalization methods to provide a rank-order of prediction for each differentially expressed sfd-RNA. By systematically addressing the three main problems, we could detect differentially expressed miRNAs and sfd-RNAs in dengue virus-infected human dermal microvascular endothelial cells. Although more biological evaluations are required, two molecular targets of the hsa-mir-103a and hsa-mir-494 (CDK5 and PI3/AKT) appear relevant for dengue virus (DENV) infections. Here, we performed a comprehensive annotation and differential expression analysis, which can be applied in other studies addressing the role of small fragment RNA populations derived from ncRNAs in virus infection.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10864640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139736873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-26DOI: 10.3389/fbinf.2024.1321508
I. Filella-Merce, Vincent Mallet, Eric Durand, Michael Nilges, G. Bouvier, Riccardo Pellarin
The current richness of sequence data needs efficient methodologies to display and analyze the complexity of the information in a compact and readable manner. Traditionally, phylogenetic trees and sequence similarity networks have been used to display and analyze sequences of protein families. These methods aim to shed light on key computational biology problems such as sequence classification and functional inference. Here, we present a new methodology, AlignScape, based on self-organizing maps. AlignScape is applied to three large families of proteins: the kinases and GPCRs from human, and bacterial T6SS proteins. AlignScape provides a map of the similarity landscape and a tree representation of multiple sequence alignments These representations are useful to display, cluster, and classify sequences as well as identify functional trends. The efficient GPU implementation of AlignScape allows the analysis of large MSAs in a few minutes. Furthermore, we show how the AlignScape analysis of proteins belonging to the T6SS complex can be used to predict coevolving partners.
{"title":"AlignScape, displaying sequence similarity using self-organizing maps","authors":"I. Filella-Merce, Vincent Mallet, Eric Durand, Michael Nilges, G. Bouvier, Riccardo Pellarin","doi":"10.3389/fbinf.2024.1321508","DOIUrl":"https://doi.org/10.3389/fbinf.2024.1321508","url":null,"abstract":"The current richness of sequence data needs efficient methodologies to display and analyze the complexity of the information in a compact and readable manner. Traditionally, phylogenetic trees and sequence similarity networks have been used to display and analyze sequences of protein families. These methods aim to shed light on key computational biology problems such as sequence classification and functional inference. Here, we present a new methodology, AlignScape, based on self-organizing maps. AlignScape is applied to three large families of proteins: the kinases and GPCRs from human, and bacterial T6SS proteins. AlignScape provides a map of the similarity landscape and a tree representation of multiple sequence alignments These representations are useful to display, cluster, and classify sequences as well as identify functional trends. The efficient GPU implementation of AlignScape allows the analysis of large MSAs in a few minutes. Furthermore, we show how the AlignScape analysis of proteins belonging to the T6SS complex can be used to predict coevolving partners.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139594460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-23DOI: 10.3389/fbinf.2023.1296667
Grigorios M. Karageorgos, Sanghee Cho, E. McDonough, Chrystal Chadwick, Soumya Ghose, Jonathan R. Owens, Kyeong Joo Jung, R. Machiraju, Robert West, James D. Brooks, Parag Mallick, Fiona Ginty
Introduction: Prostate cancer is a highly heterogeneous disease, presenting varying levels of aggressiveness and response to treatment. Angiogenesis is one of the hallmarks of cancer, providing oxygen and nutrient supply to tumors. Micro vessel density has previously been correlated with higher Gleason score and poor prognosis. Manual segmentation of blood vessels (BVs) In microscopy images is challenging, time consuming and may be prone to inter-rater variabilities. In this study, an automated pipeline is presented for BV detection and distribution analysis in multiplexed prostate cancer images.Methods: A deep learning model was trained to segment BVs by combining CD31, CD34 and collagen IV images. In addition, the trained model was used to analyze the size and distribution patterns of BVs in relation to disease progression in a cohort of prostate cancer patients (N = 215).Results: The model was capable of accurately detecting and segmenting BVs, as compared to ground truth annotations provided by two reviewers. The precision (P), recall (R) and dice similarity coefficient (DSC) were equal to 0.93 (SD 0.04), 0.97 (SD 0.02) and 0.71 (SD 0.07) with respect to reviewer 1, and 0.95 (SD 0.05), 0.94 (SD 0.07) and 0.70 (SD 0.08) with respect to reviewer 2, respectively. BV count was significantly associated with 5-year recurrence (adjusted p = 0.0042), while both count and area of blood vessel were significantly associated with Gleason grade (adjusted p = 0.032 and 0.003 respectively).Discussion: The proposed methodology is anticipated to streamline and standardize BV analysis, offering additional insights into the biology of prostate cancer, with broad applicability to other cancers.
导言:前列腺癌是一种高度异质性疾病,具有不同程度的侵袭性和对治疗的反应。血管生成是癌症的标志之一,为肿瘤提供氧气和营养。微血管密度与较高的格里森评分和较差的预后有关。人工分割显微图像中的血管(BVs)具有挑战性,不仅耗时,而且容易造成评分者之间的差异。本研究提出了一种自动管道,用于多路复用前列腺癌图像中的血管检测和分布分析:方法:结合 CD31、CD34 和胶原蛋白 IV 图像,训练深度学习模型来分割 BV。此外,还利用训练好的模型分析了前列腺癌患者队列(N = 215)中与疾病进展相关的 BV 大小和分布模式:结果:与两位审稿人提供的地面实况注释相比,该模型能够准确检测和分割BV。与审稿人1相比,精确度(P)、召回率(R)和骰子相似系数(DSC)分别为0.93(标清0.04)、0.97(标清0.02)和0.71(标清0.07);与审稿人2相比,精确度(P)、召回率(R)和骰子相似系数(DSC)分别为0.95(标清0.05)、0.94(标清0.07)和0.70(标清0.08)。血管数量与 5 年复发有明显相关性(调整后 p = 0.0042),而血管数量和面积与 Gleason 等级有明显相关性(调整后 p 分别 = 0.032 和 0.003):所提出的方法有望简化和规范血管分析,为前列腺癌的生物学研究提供更多见解,并可广泛应用于其他癌症。
{"title":"Deep learning-based automated pipeline for blood vessel detection and distribution analysis in multiplexed prostate cancer images","authors":"Grigorios M. Karageorgos, Sanghee Cho, E. McDonough, Chrystal Chadwick, Soumya Ghose, Jonathan R. Owens, Kyeong Joo Jung, R. Machiraju, Robert West, James D. Brooks, Parag Mallick, Fiona Ginty","doi":"10.3389/fbinf.2023.1296667","DOIUrl":"https://doi.org/10.3389/fbinf.2023.1296667","url":null,"abstract":"Introduction: Prostate cancer is a highly heterogeneous disease, presenting varying levels of aggressiveness and response to treatment. Angiogenesis is one of the hallmarks of cancer, providing oxygen and nutrient supply to tumors. Micro vessel density has previously been correlated with higher Gleason score and poor prognosis. Manual segmentation of blood vessels (BVs) In microscopy images is challenging, time consuming and may be prone to inter-rater variabilities. In this study, an automated pipeline is presented for BV detection and distribution analysis in multiplexed prostate cancer images.Methods: A deep learning model was trained to segment BVs by combining CD31, CD34 and collagen IV images. In addition, the trained model was used to analyze the size and distribution patterns of BVs in relation to disease progression in a cohort of prostate cancer patients (N = 215).Results: The model was capable of accurately detecting and segmenting BVs, as compared to ground truth annotations provided by two reviewers. The precision (P), recall (R) and dice similarity coefficient (DSC) were equal to 0.93 (SD 0.04), 0.97 (SD 0.02) and 0.71 (SD 0.07) with respect to reviewer 1, and 0.95 (SD 0.05), 0.94 (SD 0.07) and 0.70 (SD 0.08) with respect to reviewer 2, respectively. BV count was significantly associated with 5-year recurrence (adjusted p = 0.0042), while both count and area of blood vessel were significantly associated with Gleason grade (adjusted p = 0.032 and 0.003 respectively).Discussion: The proposed methodology is anticipated to streamline and standardize BV analysis, offering additional insights into the biology of prostate cancer, with broad applicability to other cancers.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139605164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.3389/fbinf.2023.1232671
B. Baldi, Jenny Vuong, Seán I. O’Donoghue
Introduction: When visualizing complex data, the layout method chosen can greatly affect the ability to identify outliers, spot incorrect modeling assumptions, or recognize unexpected patterns. Additionally, visual layout can play a crucial role in communicating results to peers.Methods: In this paper, we compared the effectiveness of three visual layouts—the adjacency matrix, a half-matrix layout, and a circular layout—for visualizing spatial connectivity data, e.g., contacts derived from chromatin conformation capture experiments. To assess these visual layouts, we conducted a study comprising 150 participants from Amazon’s Mechanical Turk, as well as a second expert study comprising 30 biomedical research scientists.Results: The Mechanical Turk study found that the circular layout was the most accurate and intuitive, while the expert study found that the circular and half-matrix layouts were more accurate than the matrix layout.Discussion: We concluded that the circular layout may be a good default choice for visualizing smaller datasets with relatively few spatial contacts, while, for larger datasets, the half- matrix layout may be a better choice. Our results also demonstrated how crowdsourcing methods could be used to determine which visual layouts are best for addressing specific data challenges in bioinformatics.
介绍:在对复杂数据进行可视化时,所选择的布局方法会在很大程度上影响识别异常值、发现不正确的建模假设或识别意外模式的能力。此外,可视化布局在与同行交流结果时也能起到至关重要的作用:在本文中,我们比较了三种可视化布局--邻接矩阵、半矩阵布局和圆形布局--在可视化空间连通性数据(如染色质构象捕获实验得出的接触)方面的效果。为了评估这些可视化布局,我们进行了一项由亚马逊 Mechanical Turk 的 150 名参与者组成的研究,以及第二项由 30 名生物医学研究科学家组成的专家研究:结果:Mechanical Turk 研究发现,圆形布局最准确、最直观,而专家研究发现,圆形和半矩阵布局比矩阵布局更准确:讨论:我们的结论是,对于空间接触相对较少的较小数据集,圆形布局可能是可视化的良好默认选择,而对于较大的数据集,半矩阵布局可能是更好的选择。我们的研究结果还展示了如何利用众包方法来确定哪种可视化布局最适合应对生物信息学中的特定数据挑战。
{"title":"Assessing 2D visual encoding of 3D spatial connectivity","authors":"B. Baldi, Jenny Vuong, Seán I. O’Donoghue","doi":"10.3389/fbinf.2023.1232671","DOIUrl":"https://doi.org/10.3389/fbinf.2023.1232671","url":null,"abstract":"Introduction: When visualizing complex data, the layout method chosen can greatly affect the ability to identify outliers, spot incorrect modeling assumptions, or recognize unexpected patterns. Additionally, visual layout can play a crucial role in communicating results to peers.Methods: In this paper, we compared the effectiveness of three visual layouts—the adjacency matrix, a half-matrix layout, and a circular layout—for visualizing spatial connectivity data, e.g., contacts derived from chromatin conformation capture experiments. To assess these visual layouts, we conducted a study comprising 150 participants from Amazon’s Mechanical Turk, as well as a second expert study comprising 30 biomedical research scientists.Results: The Mechanical Turk study found that the circular layout was the most accurate and intuitive, while the expert study found that the circular and half-matrix layouts were more accurate than the matrix layout.Discussion: We concluded that the circular layout may be a good default choice for visualizing smaller datasets with relatively few spatial contacts, while, for larger datasets, the half- matrix layout may be a better choice. Our results also demonstrated how crowdsourcing methods could be used to determine which visual layouts are best for addressing specific data challenges in bioinformatics.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139607121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.3389/fbinf.2023.1287168
C. Caudai, Emanuele Salerno
A multiscale method proposed elsewhere for reconstructing plausible 3D configurations of the chromatin in cell nuclei is recalled, based on the integration of contact data from Hi-C experiments and additional information coming from ChIP-seq, RNA-seq and ChIA-PET experiments. Provided that the additional data come from independent experiments, this kind of approach is supposed to leverage them to complement possibly noisy, biased or missing Hi-C records. When the different data sources are mutually concurrent, the resulting solutions are corroborated; otherwise, their validity would be weakened. Here, a problem of reliability arises, entailing an appropriate choice of the relative weights to be assigned to the different informational contributions. A series of experiments is presented that help to quantify the advantages and the limitations offered by this strategy. Whereas the advantages in accuracy are not always significant, the case of missing Hi-C data demonstrates the effectiveness of additional information in reconstructing the highly packed segments of the structure.
{"title":"Complementing Hi-C information for 3D chromatin reconstruction by ChromStruct","authors":"C. Caudai, Emanuele Salerno","doi":"10.3389/fbinf.2023.1287168","DOIUrl":"https://doi.org/10.3389/fbinf.2023.1287168","url":null,"abstract":"A multiscale method proposed elsewhere for reconstructing plausible 3D configurations of the chromatin in cell nuclei is recalled, based on the integration of contact data from Hi-C experiments and additional information coming from ChIP-seq, RNA-seq and ChIA-PET experiments. Provided that the additional data come from independent experiments, this kind of approach is supposed to leverage them to complement possibly noisy, biased or missing Hi-C records. When the different data sources are mutually concurrent, the resulting solutions are corroborated; otherwise, their validity would be weakened. Here, a problem of reliability arises, entailing an appropriate choice of the relative weights to be assigned to the different informational contributions. A series of experiments is presented that help to quantify the advantages and the limitations offered by this strategy. Whereas the advantages in accuracy are not always significant, the case of missing Hi-C data demonstrates the effectiveness of additional information in reconstructing the highly packed segments of the structure.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139608730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15eCollection Date: 2023-01-01DOI: 10.3389/fbinf.2023.1328262
Anush Karampuri, Shyam Perugu
Breast cancer is the most prevalent and heterogeneous form of cancer affecting women worldwide. Various therapeutic strategies are in practice based on the extent of disease spread, such as surgery, chemotherapy, radiotherapy, and immunotherapy. Combinational therapy is another strategy that has proven to be effective in controlling cancer progression. Administration of Anchor drug, a well-established primary therapeutic agent with known efficacy for specific targets, with Library drug, a supplementary drug to enhance the efficacy of anchor drugs and broaden the therapeutic approach. Our work focused on harnessing regression-based Machine learning (ML) and deep learning (DL) algorithms to develop a structure-activity relationship between the molecular descriptors of drug pairs and their combined biological activity through a QSAR (Quantitative structure-activity relationship) model. 11 popularly known machine learning and deep learning algorithms were used to develop QSAR models. A total of 52 breast cancer cell lines, 25 anchor drugs, and 51 library drugs were considered in developing the QSAR model. It was observed that Deep Neural Networks (DNNs) achieved an impressive R2 (Coefficient of Determination) of 0.94, with an RMSE (Root Mean Square Error) value of 0.255, making it the most effective algorithm for developing a structure-activity relationship with strong generalization capabilities. In conclusion, applying combinational therapy alongside ML and DL techniques represents a promising approach to combating breast cancer.
{"title":"A breast cancer-specific combinational QSAR model development using machine learning and deep learning approaches.","authors":"Anush Karampuri, Shyam Perugu","doi":"10.3389/fbinf.2023.1328262","DOIUrl":"10.3389/fbinf.2023.1328262","url":null,"abstract":"<p><p>Breast cancer is the most prevalent and heterogeneous form of cancer affecting women worldwide. Various therapeutic strategies are in practice based on the extent of disease spread, such as surgery, chemotherapy, radiotherapy, and immunotherapy. Combinational therapy is another strategy that has proven to be effective in controlling cancer progression. Administration of Anchor drug, a well-established primary therapeutic agent with known efficacy for specific targets, with Library drug, a supplementary drug to enhance the efficacy of anchor drugs and broaden the therapeutic approach. Our work focused on harnessing regression-based Machine learning (ML) and deep learning (DL) algorithms to develop a structure-activity relationship between the molecular descriptors of drug pairs and their combined biological activity through a QSAR (Quantitative structure-activity relationship) model. 11 popularly known machine learning and deep learning algorithms were used to develop QSAR models. A total of 52 breast cancer cell lines, 25 anchor drugs, and 51 library drugs were considered in developing the QSAR model. It was observed that Deep Neural Networks (DNNs) achieved an impressive R<sup>2</sup> (Coefficient of Determination) of 0.94, with an RMSE (Root Mean Square Error) value of 0.255, making it the most effective algorithm for developing a structure-activity relationship with strong generalization capabilities. In conclusion, applying combinational therapy alongside ML and DL techniques represents a promising approach to combating breast cancer.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10822965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139577087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-10DOI: 10.3389/fbinf.2023.1284705
Suraiya Akhter, John H. Miller
The use of bacteriocins has emerged as a propitious strategy in the development of new drugs to combat antibiotic resistance, given their ability to kill bacteria with both broad and narrow natural spectra. Hence, a compelling requirement arises for a precise and efficient computational model that can accurately predict novel bacteriocins. Machine learning’s ability to learn patterns and features from bacteriocin sequences that are difficult to capture using sequence matching-based methods makes it a potentially superior choice for accurate prediction. A web application for predicting bacteriocin was created in this study, utilizing a machine learning approach. The feature sets employed in the application were chosen using alternating decision tree (ADTree), genetic algorithm (GA), and linear support vector classifier (linear SVC)-based feature evaluation methods. Initially, potential features were extracted from the physicochemical, structural, and sequence-profile attributes of both bacteriocin and non-bacteriocin protein sequences. We assessed the candidate features first using the Pearson correlation coefficient, followed by separate evaluations with ADTree, GA, and linear SVC to eliminate unnecessary features. Finally, we constructed random forest (RF), support vector machine (SVM), decision tree (DT), logistic regression (LR), k-nearest neighbors (KNN), and Gaussian naïve Bayes (GNB) models using reduced feature sets. We obtained the overall top performing model using SVM with ADTree-reduced features, achieving an accuracy of 99.11% and an AUC value of 0.9984 on the testing dataset. We also assessed the predictive capabilities of our best-performing models for each reduced feature set relative to our previously developed software solution, a sequence alignment-based tool, and a deep-learning approach. A web application, titled BPAGS (Bacteriocin Prediction based on ADTree, GA, and linear SVC), was developed to incorporate the predictive models built using ADTree, GA, and linear SVC-based feature sets. Currently, the web-based tool provides classification results with associated probability values and has options to add new samples in the training data to improve the predictive efficacy. BPAGS is freely accessible at https://shiny.tricities.wsu.edu/bacteriocin-prediction/.
{"title":"BPAGS: a web application for bacteriocin prediction via feature evaluation using alternating decision tree, genetic algorithm, and linear support vector classifier","authors":"Suraiya Akhter, John H. Miller","doi":"10.3389/fbinf.2023.1284705","DOIUrl":"https://doi.org/10.3389/fbinf.2023.1284705","url":null,"abstract":"The use of bacteriocins has emerged as a propitious strategy in the development of new drugs to combat antibiotic resistance, given their ability to kill bacteria with both broad and narrow natural spectra. Hence, a compelling requirement arises for a precise and efficient computational model that can accurately predict novel bacteriocins. Machine learning’s ability to learn patterns and features from bacteriocin sequences that are difficult to capture using sequence matching-based methods makes it a potentially superior choice for accurate prediction. A web application for predicting bacteriocin was created in this study, utilizing a machine learning approach. The feature sets employed in the application were chosen using alternating decision tree (ADTree), genetic algorithm (GA), and linear support vector classifier (linear SVC)-based feature evaluation methods. Initially, potential features were extracted from the physicochemical, structural, and sequence-profile attributes of both bacteriocin and non-bacteriocin protein sequences. We assessed the candidate features first using the Pearson correlation coefficient, followed by separate evaluations with ADTree, GA, and linear SVC to eliminate unnecessary features. Finally, we constructed random forest (RF), support vector machine (SVM), decision tree (DT), logistic regression (LR), k-nearest neighbors (KNN), and Gaussian naïve Bayes (GNB) models using reduced feature sets. We obtained the overall top performing model using SVM with ADTree-reduced features, achieving an accuracy of 99.11% and an AUC value of 0.9984 on the testing dataset. We also assessed the predictive capabilities of our best-performing models for each reduced feature set relative to our previously developed software solution, a sequence alignment-based tool, and a deep-learning approach. A web application, titled BPAGS (Bacteriocin Prediction based on ADTree, GA, and linear SVC), was developed to incorporate the predictive models built using ADTree, GA, and linear SVC-based feature sets. Currently, the web-based tool provides classification results with associated probability values and has options to add new samples in the training data to improve the predictive efficacy. BPAGS is freely accessible at https://shiny.tricities.wsu.edu/bacteriocin-prediction/.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-08DOI: 10.3389/fbinf.2023.1332902
Prajay Patel, Nisha Pillai, Inimary T. Toby
No-boundary thinking enables the scientific community to reflect in a thoughtful manner and discover new opportunities, create innovative solutions, and break through barriers that might have otherwise constrained their progress. This concept encourages thinking without being confined by traditional rules, limitations, or established norms, and a mindset that is not limited by previous work, leading to fresh perspectives and innovative outcomes. So, where do we see the field of artificial intelligence (AI) in bioinformatics going in the next 30 years? That was the theme of a “No-Boundary Thinking” Session as part of the Mid-South Computational Bioinformatics Society’s (MCBIOS) 19th annual meeting in Irving, Texas. This session addressed various areas of AI in an open discussion and raised some perspectives on how popular tools like ChatGPT can be integrated into bioinformatics, communicating with scientists in different fields to properly utilize the potential of these algorithms, and how to continue educational outreach to further interest of data science and informatics to the next-generation of scientists.
{"title":"No-boundary thinking for artificial intelligence in bioinformatics and education","authors":"Prajay Patel, Nisha Pillai, Inimary T. Toby","doi":"10.3389/fbinf.2023.1332902","DOIUrl":"https://doi.org/10.3389/fbinf.2023.1332902","url":null,"abstract":"No-boundary thinking enables the scientific community to reflect in a thoughtful manner and discover new opportunities, create innovative solutions, and break through barriers that might have otherwise constrained their progress. This concept encourages thinking without being confined by traditional rules, limitations, or established norms, and a mindset that is not limited by previous work, leading to fresh perspectives and innovative outcomes. So, where do we see the field of artificial intelligence (AI) in bioinformatics going in the next 30 years? That was the theme of a “No-Boundary Thinking” Session as part of the Mid-South Computational Bioinformatics Society’s (MCBIOS) 19th annual meeting in Irving, Texas. This session addressed various areas of AI in an open discussion and raised some perspectives on how popular tools like ChatGPT can be integrated into bioinformatics, communicating with scientists in different fields to properly utilize the potential of these algorithms, and how to continue educational outreach to further interest of data science and informatics to the next-generation of scientists.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}