diff --git a/joss.04265/10.21105.joss.04265.crossref.xml b/joss.04265/10.21105.joss.04265.crossref.xml new file mode 100644 index 0000000000..212dfc82c6 --- /dev/null +++ b/joss.04265/10.21105.joss.04265.crossref.xml @@ -0,0 +1,238 @@ + + + + 20220601T054500-ff49e188793b57116d9f56a318ada3d2a0e7ed94 + 20220601054500 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org/ + + + + + 06 + 2022 + + + 7 + + 74 + + + + MitoHEAR: an R package for the estimation and +downstream statistical analysis of the mitochondrial DNA heteroplasmy +calculated from single-cell datasets + + + + Gabriele + Lubatti + + + Elmir + Mahammadov + + + Antonio + Scialdone + + + + 06 + 01 + 2022 + + + 4265 + + + 10.21105/joss.04265 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.6598821 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/4265 + + + + 10.21105/joss.04265 + https://joss.theoj.org/papers/10.21105/joss.04265 + + + https://joss.theoj.org/papers/10.21105/joss.04265.pdf + + + + + + Mitochondrial variant enrichment from +high-throughput single-cell RNA-seq resolves clonal +populations + Miller + Nature Biotechnology + 10.1038/s41587-022-01210-8 + 2022 + Miller, T. E., Lareau, C. A., Verga, +J. A., Ssozi, D., Ludwig, L. S., Farran, C. E., Griffin, G. K., Lane, A. +A., Bernstein, B. E., Sankaran, V. G., & van Galen, P. (2022). +Mitochondrial variant enrichment from high-throughput single-cell +RNA-seq resolves clonal populations. Nature Biotechnology. +https://doi.org/10.1038/s41587-022-01210-8 + + + Lineage tracing in humans enabled by +mitochondrial mutations and single-cell genomics + Ludwig + Cell + 6 + 176 + 10.1016/j.cell.2019.01.022 + 0092-8674 + 2019 + Ludwig, L. S., Lareau, C. A., +Ulirsch, J. C., Christian, E., Muus, C., Li, L. H., Pelka, K., Ge, W., +Oren, Y., Brack, A., Law, T., Rodman, C., Chen, J. H., Boland, G. M., +Hacohen, N., Rozenblatt-Rosen, O., Aryee, M. J., Buenrostro, J. D., +Regev, A., & Sankaran, V. G. (2019). Lineage tracing in humans +enabled by mitochondrial mutations and single-cell genomics. Cell, +176(6), 1325–1339.e22. +https://doi.org/10.1016/j.cell.2019.01.022 + + + Cellsnp-lite: an efficient tool for +genotyping single cells + Huang + Bioinformatics + 10.1093/bioinformatics/btab358 + 1367-4803 + 2021 + Huang, X., & Huang, Y. (2021). +Cellsnp-lite: an efficient tool for genotyping single cells. +Bioinformatics. +https://doi.org/10.1093/bioinformatics/btab358 + + + SCReadCounts: Estimation of cell-level SNVs +expression from scRNA-seq data + Prashant + BMC Genomics + 10.1186/s12864-021-07974-8 + 2021 + Prashant, N., Alomran, N., Chen, Y., +Liu, H., Bousounis, P., Movassagh, M., Edwards, N., & Horvath, A. +(2021). SCReadCounts: Estimation of cell-level SNVs expression from +scRNA-seq data. BMC Genomics. +https://doi.org/10.1186/s12864-021-07974-8 + + + Cell competition acts as a purifying +selection to eliminate cells with mitochondrial defects during early +mouse development + Lima + Nature Metabolism + 10.1038/s42255-021-00422-7 + 2021 + Lima, A., Lubatti, G., Burgstaller, +J., Hu, D., Green, A., Gregorio, A. D., Zawadzki, T., Pernaute, B., +Mahammadov, E., Dore, M., Sanchez, J. M., Bowling, S., Sancho, M., +Karimi, M., Carling, D., Jones, N., Srinivas, S., Scialdone, A., & +Rodriguez, T. A. (2021). Cell competition acts as a purifying selection +to eliminate cells with mitochondrial defects during early mouse +development. Nature Metabolism. +https://doi.org/10.1038/s42255-021-00422-7 + + + Mitochondrial DNA heteroplasmy in disease and +targeted nuclease-based therapeutic approaches + Nissanka + EMBO Reports + 3 + 21 + 10.15252/embr.201949612 + 2020 + Nissanka, N., & Moraes, C. T. +(2020). Mitochondrial DNA heteroplasmy in disease and targeted +nuclease-based therapeutic approaches. EMBO Reports, 21(3), e49612. +https://doi.org/10.15252/embr.201949612 + + + The dynamics of mitochondrial DNA +heteroplasmy: Implications for human health and disease + Stewart + Nature Reviews Genetics + 10.1038/nrg3966 + 2015 + Stewart, J., & Chinnery, P. +(2015). The dynamics of mitochondrial DNA heteroplasmy: Implications for +human health and disease. Nature Reviews Genetics. +https://doi.org/10.1038/nrg3966 + + + Segregation of mitochondrial DNA heteroplasmy +through a developmental genetic bottleneck in human +embryos + Floros + Nature Cell Biology + 10.1038/s41556-017-0017-8 + 2019 + Floros, V., Pyle, A., Dietmann, S., +Wei, W., Tang, W., Irie, N., Payne, B., Capalbo, A., Noli, L., Coxhead, +J., Hudson, G., Crosier, M., Strahl, H., Khalaf, Y., Saitou, M., Ilic, +D., Surani, M., & Chinnery, P. (2019). Segregation of mitochondrial +DNA heteroplasmy through a developmental genetic bottleneck in human +embryos. Nature Cell Biology. +https://doi.org/10.1038/s41556-017-0017-8 + + + MToolBox: a highly automated pipeline for +heteroplasmy annotation and prioritization analysis of human +mitochondrial variants in high-throughput sequencing + Calabrese + Bioinformatics + 21 + 30 + 10.1093/bioinformatics/btu483 + 1367-4803 + 2014 + Calabrese, C., Simone, D., Diroma, M. +A., Santorsola, M., Guttà, C., Gasparre, G., Picardi, E., Pesole, G., +& Attimonelli, M. (2014). MToolBox: a highly automated pipeline for +heteroplasmy annotation and prioritization analysis of human +mitochondrial variants in high-throughput sequencing. Bioinformatics, +30(21), 3115–3117. +https://doi.org/10.1093/bioinformatics/btu483 + + + + + + diff --git a/joss.04265/10.21105.joss.04265.jats b/joss.04265/10.21105.joss.04265.jats new file mode 100644 index 0000000000..70bbd932cb --- /dev/null +++ b/joss.04265/10.21105.joss.04265.jats @@ -0,0 +1,452 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +4265 +10.21105/joss.04265 + +MitoHEAR: an R package for the estimation and downstream +statistical analysis of the mitochondrial DNA heteroplasmy calculated +from single-cell datasets + + + + +Lubatti +Gabriele + + + + + + + +Mahammadov +Elmir + + + + + + + +Scialdone +Antonio + + + + +* + + + +Institute of Epigenetics and Stem Cells, Helmholtz Zentrum +München, Munich, Germany + + + + +Institute of Functional Epigenetics, Helmholtz Zentrum +München, Neuherberg, Germany + + + + +Institute of Computational Biology, Helmholtz Zentrum +München, Neuherberg, Germany + + + + +* E-mail: + + +17 +6 +2021 + +7 +74 +4265 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +R +bioinformatics +single cell RNA seq +heteroplasmy + + + + + + Summary +

Eukaryotic cells rely on mitochondria, organelles that are equipped + with their own DNA (mtDNA) to produce the energy they need. Each cell + includes multiple mtDNA copies that are not perfectly identical but + have differences in their sequence; such sequence variability is + called heteroplasmy. mtDNA heteroplasmy has been associated with + diseases + (Nissanka + & Moraes, 2020), that can affect cellular fitness and have + an impact on cellular competition + (Lima + et al., 2021). Several single-cell sequencing protocols provide + the data to estimate mtDNA heteroplasmy, including single-cell + DNA-seq, RNA-seq and ATAC-seq, in addition to dedicated protocols like + MAESTER + (Miller + et al., 2022). Here, we provide MitoHEAR (Mitochondrial + HEteroplasmy AnalyzeR), a user-friendly software written in R that + allows the estimation as well as downstream statistical analysis of + the mtDNA heteroplasmy calculated from single-cell datasets. MitoHEAR + takes as input BAM files, computes the frequency of each allele and, + starting from these, estimates the mtDNA heteroplasmy at each covered + position for each cell. + The analysis parameters (e.g., the filtering of the mtDNA positions + based on read quality and coverage) are easily tuneable. Moreover, + statistical tests are available to explore the dependency of the mtDNA + heteroplasmy on continuous or discrete cell covariates (e.g., culture + conditions, differentiation states, etc), as extensively shown in the + detailed tutorials we include.

+ + + Statement of need +

Although mtDNA heteroplasmy has important consequences on human + health + (Stewart + & Chinnery, 2015) and embryonic development + (Floros + et al., 2019), there are still many open questions on how + heteroplasmy affects cells’ ability to function and how cells keep it + under control. With the increasing availability of single-cell data, + many questions can begin to be answered. Still, it is fundamental to + have efficient and streamlined computational tools enabling + researchers to estimate and analyse mtDNA heteroplasmy. Existing + packages + (Calabrese + et al., 2014; + Huang + & Huang, 2021; + Prashant + et al., 2021) focus only on the first step of quantifying + heteroplasmy from BAM files, and do not provide any specific tools for + further statistical analyses or plotting. Instead, MitoHEAR covers all + steps of the analysis in a unique user-friendly package, with highly + customisable functions. Starting from BAM files, MitoHEAR estimates + heteroplasmy and offers several options for downstream analyses. For + example, statistical tests are provided to investigate the + relationship of the mtDNA heteroplasmy with continuous or discrete + cell covariates. Moreover, there are plotting functions to visualise + heteroplasmy and allele frequencies and to perform hierarchical + clustering of cells based on heteroplasmy values.

+ + + Key functions +

The two main functions of MitoHEAR are:

+ + +

get_raw_counts_allele: a parallelised + function that relies on Rsamtools and generates the raw counts + matrix starting from BAM files, with cells as rows and bases with + the four possible alleles as columns.

+ + +

get_heteroplasmy: Starting from the + output of get_raw_counts_allele, it + computes the matrix with heteroplasmy values (defined as 1 minus + the frequency of the most common allele) and the matrix with + allele frequency values, for all the cells and bases that pass a + filtering procedure.

+ + +

Among the downstream analyses implemented in the package are:

+ + +

Several statistical tests (e.g., Wilcoxon rank-sum test) for + the identification of the mtDNA positions with the most different + levels of heteroplasmy between discrete groups of cells or along a + trajectory of cells (i.e., cells sorted according to a diffusion + pseudo-time) (Figure 1 and Figure + 2).

+ + +

Plotting functions for the visualisation of heteroplasmy and + the corresponding allele frequency values among cells.

+ + +

Unsupervised hierarchical clustering of cells based on a + distance matrix defined from the angular distance of allele + frequencies that could be relevant for lineage tracing analysis + (Ludwig + et al., 2019) (Figure 3).

+ + + +

Example of an output plot generated by MitoHEAR showing + heteroplasmy values at a given position estimated from single cells + in three clusters indicated on the x-axis. Data from Lima et al. + (2021). +

+ + + +

Example of an output figure generated by MitoHEAR where + the heteroplasmy is plotted as a function of the pseudo-time + coordinate of each cell. Cells are classified into three clusters. + The heteroplasmy shows a statistically significant change along the + pseudo-time, as indicated by the adjusted p-value reported at the + top, which is computed by a generalised additive model fit. Data + from Lima et al. + (2021). +

+ + + +

Unsupervised hierarchical clustering of cells based on a + distance matrix defined from the angular distance of allele + frequencies. The data shown is bulk RNA-seq mouse data from two + mtDNA cell lines labelled Loser and + Winner. Data from Lima et al. + (2021). +

+ + +

The package has been used in a recently published paper + (Lima + et al., 2021), where we revealed that cells with higher levels + of heteroplasmy are eliminated by cell competition in mouse embryos + and are characterised by specific gene expression patterns.

+ + + + + + + + MillerTyler E. + LareauCaleb A. + VergaJulia A. + SsoziDaniel + LudwigLeif S. + FarranChadi El + GriffinGabriel K. + LaneAndrew A. + BernsteinBradley E. + SankaranVijay G. + van GalenPeter + + Mitochondrial variant enrichment from high-throughput single-cell RNA-seq resolves clonal populations + Nature Biotechnology + 2022 + https://www.nature.com/articles/s41587-022-01210-8 + 10.1038/s41587-022-01210-8 + + + + + + LudwigLeif S. + LareauCaleb A. + UlirschJacob C. + ChristianElena + MuusChristoph + LiLauren H. + PelkaKarin + GeWill + OrenYaara + BrackAlison + LawTravis + RodmanChristopher + ChenJonathan H. + BolandGenevieve M. + HacohenNir + Rozenblatt-RosenOrit + AryeeMartin J. + BuenrostroJason D. + RegevAviv + SankaranVijay G. + + Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics + Cell + 2019 + 176 + 6 + 0092-8674 + https://www.sciencedirect.com/science/article/pii/S0092867419300558 + 10.1016/j.cell.2019.01.022 + 1325 + 1339.e22 + + + + + + HuangXianjie + HuangYuanhua + + Cellsnp-lite: an efficient tool for genotyping single cells + Bioinformatics + 202105 + 1367-4803 + https://doi.org/10.1093/bioinformatics/btab358 + 10.1093/bioinformatics/btab358 + + + + + + PrashantNM + AlomranNawaf + ChenYu + LiuHongyu + BousounisPavlos + MovassaghMercedeh + EdwardsNathan + HorvathAnelia + + SCReadCounts: Estimation of cell-level SNVs expression from scRNA-seq data + BMC Genomics + 2021 + https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-021-07974-8 + 10.1186/s12864-021-07974-8 + + + + + + LimaAna + LubattiGabriele + BurgstallerJörg + HuDi + GreenAlistair + GregorioAida Di + ZawadzkiTamzin + PernauteBarbara + MahammadovElmir + DoreMarian + SanchezJuan Miguel + BowlingSarah + SanchoMargarida + KarimiMohammad + CarlingDavid + JonesNick + SrinivasShankar + ScialdoneAntonio + RodriguezTristan A. + + Cell competition acts as a purifying selection to eliminate cells with mitochondrial defects during early mouse development + Nature Metabolism + 2021 + https://www.nature.com/articles/s42255-021-00422-7 + 10.1038/s42255-021-00422-7 + + + + + + NissankaNadee + MoraesCarlos T + + Mitochondrial DNA heteroplasmy in disease and targeted nuclease-based therapeutic approaches + EMBO Reports + 2020 + 21 + 3 + https://www.embopress.org/doi/abs/10.15252/embr.201949612 + 10.15252/embr.201949612 + e49612 + + + + + + + StewartJ + ChinneryP + + The dynamics of mitochondrial DNA heteroplasmy: Implications for human health and disease + Nature Reviews Genetics + 2015 + 10.1038/nrg3966 + + + + + + FlorosVI + PyleA + DietmannS + WeiW + TangWCW + IrieN + PayneB + CapalboA + NoliL + CoxheadJ + HudsonG + CrosierM + StrahlH + KhalafY + SaitouM + IlicD + SuraniMA + ChinneryPF + + Segregation of mitochondrial DNA heteroplasmy through a developmental genetic bottleneck in human embryos + Nature Cell Biology + 2019 + 10.1038/s41556-017-0017-8 + + + + + + CalabreseClaudia + SimoneDomenico + DiromaMaria Angela + SantorsolaMariangela + GuttàCristiano + GasparreGiuseppe + PicardiErnesto + PesoleGraziano + AttimonelliMarcella + + MToolBox: a highly automated pipeline for heteroplasmy annotation and prioritization analysis of human mitochondrial variants in high-throughput sequencing + Bioinformatics + 201407 + 30 + 21 + 1367-4803 + https://doi.org/10.1093/bioinformatics/btu483 + 10.1093/bioinformatics/btu483 + 3115 + 3117 + + + + +
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