Fruit fly gtf file download

The modular design of SPR makes it easy to design custom functions without any knowledge of Shiny, as well as extending the environment in the future with contributions from the community. It takes all of the main types of HiC input and returns simple, comprehensive, easy to analyze results. This tool can generate small networks within R or export results into cytoscape or pathvisio for more detailed network construction and hypothesis generation.

This tool is created for researchers that wish to dive deep into time series multi-omic datasets. TimiRGeN goes further than many other tools in terms of data reduction. The tomo-seq technique is based on cryosectioning of tissue and performing RNA-seq on consecutive sections. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. Several visulization functions are available to create easy-to-modify plots.

ToxicoGx Contains a set of functions to perform large-scale analysis of toxicogenomic data, providing a standardized data structure to hold information relevant to annotation, visualization and statistical analysis of toxicogenomic data. The edges are used, for example, to describe the relationships between kinase on a pathway and enzyme on another pathway. This package automates creation of a transomics network as shown in the figure in Yugi.

UMI4Cats UMI-4C is a technique that allows characterization of 3D chromatin interactions with a bait of interest, taking advantage of a sonication step to produce unique molecular identifiers UMIs that help remove duplication bias, thus allowing a better differential comparsion of chromatin interactions between conditions.

It provides two statistical methods for detecting differential contacts and includes a visualization function to plot integrated information from a UMI-4C assay. It displays three web pages each providing a different analysis module: Coverage analysis, calculate AF by allele frequency app and binomial distribution. We use the basilisk package to manage Conda environments, and the zellkonverter package to convert data structures between SingleCellExperiment R and AnnData Python.

The information produced by the velocity methods is stored in the various components of the SingleCellExperiment class. VERSO Mutations that rapidly accumulate in viral genomes during a pandemic can be used to track the evolution of the virus and, accordingly, unravel the viral infection network.

To this extent, sequencing samples of the virus can be employed to estimate models from genomic epidemiology and may serve, for instance, to estimate the proportion of undetected infected people by uncovering cryptic transmissions, as well as to predict likely trends in the number of infected, hospitalized, dead and recovered people. VERSO is an algorithmic framework that processes variants profiles from viral samples to produce phylogenetic models of viral evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a log-likelihood function.

This package contains useful functions to analyze patterns of paired-end sequencing fragment density. VplotR facilitates the generation of V-plots and footprint profiles over single or aggregated genomic loci of interest. It uses a backtracking-inspired algorithm to place samples on plates based on specific neighborhood constraints.

These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. DropletTestFiles Assorted files generated from droplet-based single-cell protocols, to be used for testing functions in DropletUtils. Unlike other packages, this is not designed to provide objects that are immediately ready for analysis. FieldEffectCrc Processed RNA-seq data for 1, human primary colorectal tissue samples across three phenotypes, including tumor, normal adjacent-to-tumor, and healthy, available as Synapse ID syn on synapse.

Data have been parsed into SummarizedExperiment objects available via ExperimentHub to facilitate reproducibility and extension of results from Dampier et al. MethylSeqData Base-level i. Reads are aligned to chromosome 22 Grch38 and stored as bam files.

The original data is from the SG-NEx project. The first dataset, from Shoemaker et al, consists of microarray samples from lung tissue of mice exposed to different influenzy strains from 14 timepoints. The two other datasets are leaf and root samples from sorghum crops exposed to pre- and post-flowering drought stress and a control condition, sampled across the plants lifetime. TMExplorer This package provides a tool to search and download a collection of tumour microenvironment single-cell RNA sequencing datasets and their metadata.

TMExplorer aims to act as a single point of entry for users looking to study the tumour microenvironment at the single cell level. Users can quickly search available datasets using the metadata table and then download the ones they are interested in for analysis.

Objects containing species, phylogenetic trees, and orthology information of eukaryotes from different orthologous databases are provided. There are 2 online books in this release of Bioconductor. While the OSCA book has been around for a longer period of time than this release, this is the first release where the book is being hosted, built. This book will teach you how to make use of cutting-edge Bioconductor tools to process, analyze, visualize, and explore scRNA-seq data.

SingleRBook This book covers the use of SingleR, one implementation of an automated annotation method. If you want a survey of different annotation methods - this book is not for you. If you want to create hand-crafted cluster definitions - this book is not for you. Read the other one instead. If you want to use the pre-Bioconductor version of the package - this book is not for you.

Too many changes to be listed here. Check the vignette for a summary of all the features. This is the right approach to serialize single R objects. Sorry for the inconvenience, but the sooner we fix this the better. Unless you used the. This means we now use a tidy evaluation syntax for filter. The Bruker title file has quite a free format definition. The heuristics to parse the title file have been improved.

Depend on tidyr 0. Insert metadata to use the R kernel. Rmd does not process markdown well enough, e. MSstats messages are not displayed by default when using artmsQuantification.

Bams files are proccesed one by one, according target object. It improves running time and memory usage. There are new functions for exportiing results into HTML pages. Genome viewer plots will now show the forward strand above the reverse strand, and reading frames will now be laid out in 1 through 6 order moving down the genome viewer before it was reversed.

Fixed a bug that was causing an error when users attempted to zoom out ten-fold with the genome viewer plot. For the latter, use as. Make use of Parameter argument consistent across several methods, where Param and Which were used before. Moved to store molecule counts in rowData altExp sce rather than metadata sce. Also use a slightly more principled calculation internally. This function is likely to be deprecated. Updates default probability threshold in. This is now set to ProbThreshold not 0.

This includes. VGPlot ,. VGGridPlot ,. VGGridPlot and. Also create functions to disply relationships of an OBOSet object. Do strict start of and end of string check for formatting. Improved description in the tutorial BioMM function returns the prediction scores instead of metrics. Added new deltaW calculator that allows to define multiplication of the original window size to use for deltaW calculations.

Update end coordinates before start coordinates in the function. If desired genes are entered as a vector, they are converted to a list without returning an error. Corrected format check for experiment.

Merge documentation into one man page for junction, coverage and outlier processing functions to reduce runtime of roxygen examples. SparseArraySeed objects now can hold dimnames. These functions are effectiveGrid , currentBlockId , and currentViewport. Various fixes and improvements to block processing of sparse logical DelayedMatrix objects e. DelayedMatrix object with a lgCMatrix seed from thr Matrix package. DelayedMatrixStats now imports the generics from MatrixGenerics.

Major overhaul of dispersion estimation and GLM estimation functions from Constantin Ahlmann-Eltze, which will allow use of the glmGamPoi package from within DESeq2, in particular relevant for single-cell datasets. In addition, the dispersion estimation is more accurate for genes with many small counts, as found in single-cell datasets.

See glmGamPoi manuscript for details on methods, doi: Interactive only. The main upgrade involves how the modelling and normalization are done. The previous methods for modelling are maintained for backward compatibility, however they are not the default.

To repeat earlier analyses, dba. DiffBind3 for more information. The default mode for dba. Normalization options have been moved from dba. Any non-default normalization options must be specified using dba. Added ComplexHeatmap integration to dittoHeatmap , controlled by a new input, complex. Added Rasterization for improved image editor compatibility of complex plots.

See the dedicated section in the vignette for details. You can also search for this author in PubMed Google Scholar. Correspondence to Cole Trapnell. Reprints and Permissions. Nat Protoc 7, — Download citation. Published : 01 March Issue Date : March Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative. BMC Plant Biology BMC Genomic Data BMC Genomics Journal of Translational Medicine By submitting a comment you agree to abide by our Terms and Community Guidelines.

If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Advanced search. Skip to main content Thank you for visiting nature. This article has been updated. Abstract Recent advances in high-throughput cDNA sequencing RNA-seq can reveal new genes and splice variants and quantify expression genome-wide in a single assay. Access through your institution. Buy or subscribe. Rent or Buy article Get time limited or full article access on ReadCube.

Figure 1: Software components used in this protocol. Figure 2: An overview of the Tuxedo protocol. Figure 3: Merging sample assemblies with a reference transcriptome annotation. Figure 4: Analyzing groups of transcripts identifies differentially regulated genes. Figure 5: CummeRbund helps users rapidly explore their expression data and create publication-ready plots of differentially expressed and regulated genes. Figure 6: CummeRbund plots of the expression level distribution for all genes in simulated experimental conditions C1 and C2.

Figure 7: CummeRbund scatter plots highlight general similarities and specific outliers between conditions C1 and C2. Figure 8. Figure 9: Differential analysis results for regucalcin. Figure Differential analysis results for Rala. References 1 Mortazavi, A. Wu, T. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26 , — Dobin, A. Bioinformatics 29 , 15—21 Guttman, M.

Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Li, W. Grabherr, M. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Schulz, M. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels.

Bioinformatics 28 , — Xie, Y. Bioinformatics 30 , — Li, B. BMC Bioinformatics 12 , Roberts, A. Streaming fragment assignment for real-time analysis of sequencing experiments. Methods 10 , 71—73 Patro, R.

Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Robinson, M. Love, M. Differential analysis of gene regulation at transcript resolution with RNA-seq. Anders, S. Detecting differential usage of exons from RNA-seq data. A Sequence alignment of transgenic wmk homologs.

The codon farthest on the left is the fourth codon in the sequence, and the highlighted codon is the 16th, with the farthest right representing the 23rd codon, and ellipses indicating codons continuing on either side.

The red box outlines where the genotypes cluster by phenotype. Colors correlate with amino acid identity. B Sequence alignment of transgenes with either the w Mel wmk sequence made anew w Mel wmk new , or with the 16th codon red box replaced with the synonymous codons from the w Rec and w Suzi wmk transgenes.

The colors and symbols reflect those in A. C Sex ratios of adult transgenic flies are shown for expressing Act5c -Gal4 and non-expressing CyO offspring that include the original transgene w Mel wmk strain used in previous figures, along with the newly created identical w Mel wmk new transgene and the additional transgenes with the single codon swapped out for the indicated codons noted in A.

Data and statistical outputs are available in Figure 4—source data 1 and Figure 4—source data 2 , respectively. D Gene expression in embryos 4—5 h AED denotes expression of each transgene relative to that of rp Data and statistical outputs are available in Figure 4—source data 3 and Figure 4—source data 4 , respectively.

E Predicted RNA secondary structures are shown for the w Mel wmk transcript compared to both of the w Rec or w Suzi codon transgenes exhibiting slight structural differences. The structure for transgene w Mel wmk is included again from Figure 3 for ease of comparison. Black circles highlight a key area of difference between the structures, with a stem absent in the w Suzi codon strain, and different base pair match probabilities calculated for each as indicated by color blue to red, low to high probability.

Within the black circle, the w Suzi codon transgene structure is missing a predicted stem that the others have. The stem in the w Rec codon line, while present, has a weaker prediction as noted by the cooler colors, so there may be structural differences compared to the w Mel wmk model. Data for sex ratios from expression of transgenes with single codon changes corresponding to Figure 4C. When these three otherwise identical genes were transgenically expressed, the wMel wmk new transgene with no changes caused a biased sex ratio as expected; however, and remarkably, expression of transgenes with two different Serine codons ablated the phenotype and resulted in a non-biased sex ratio with normal numbers of expressing flies Figure 4C.

This ablation occurs even though transcript levels remain similar across all transgenes and despite sequencing confirmation of the single codon differences Figure 4D. However, while the nucleotide changes in the codon changed the phenotype, they did not recapitulate the all-killing phenotypes of their corresponding homologs.

The predicted RNA secondary structures from the transgenes with the single codon changes are similar to the original w Mel wmk transgene, but they differ in some aspects such as presence or absence of stems and loops and the probability score of the base pair match as indicated by color scale of red to blue, warmer colors indicate high probability, cooler colors indicate low probability.

Figure 4E. Linking mutations to function is crucial in resolving the evolutionary dynamics of adaptations, especially when the functions emerge in a nested system of tripartite phage-bacteria-animal interactions. In a simple case, the direct impact of genetic divergence is functional divergence with increasing numbers of mutations leading to increased functional divergence. Here, we investigated evolutionary and molecular hypotheses related to the genotype-phenotype relationships between the prophage WO-mediated killing wmk gene Perlmutter et al.

Sequences among wmk homologs in nature can differ across divergent hosts, leading to the hypothesis that highly divergent alleles are functionally fine-tuned. Using a variety of sequences, we report three key results: i mutations even at the synonymous codon and single nucleotide levels can alter the male-killing phenotype, ii phenotype, genotype, and RNA structural variation exhibit some correlation with each other, and iii distantly-related homologs do not induce a male-killing phenotype in transgenic D.

We discuss how these findings expand and support mechanistic models of wmk male killing, emphasize important implications for transgenic assays in endosymbiont studies, and relate these findings to aspects of male killing such as phenotype switching and host resistance. The most surprising and unanticipated result was that transgene expression of highly similar homologs and even single synonymous site changes alter phenotype, and they make the difference between life and death for some males.

We tested wmk homologs with high native sequence identity compared to native w Mel wmk: w Rec wmk from the mushroom-feeding D.

Although we anticipated similar results to w Mel wmk expression, we found that transgenic expression of these genes killed all flies Figure 2 , even though the w Suzi wmk transgene produces an identical protein to transgenic w Mel Wmk.

Codon optimization is the norm in transgenic symbiosis research under the common assumption that synonymous codons are functionally redundant. However, codon optimization algorithms often choose a codon based on factors including codon adaptation, mRNA folding, regulatory motifs, nucleotide bias, or codon correlations and biases Plotkin and Kudla, With even a few different codons input into the algorithm as well as algorithm updates over the years, the tested transgenes had different nucleotide sequences.

Based on the aforementioned results, we next sought to assess the sensitivity of male killing to wmk transcriptional and post-transcriptional changes and asked if one synonymous codon or site change was sufficient to alter phenotype. We created three transgenic lines, each with different DNA sequences coding for the same Serine at position With only these minor changes that encode an identical protein sequence to w Mel Wmk, the transgenic male-killing phenotype was ablated Figure 4.

Thus, one single codon and even a single nucleotide, remarkably determined male viability. Importantly, this key result implies at the molecular level that endosymbiont phenotypes of reproductive parasites may not simply be governed by DNA sequence alone.

A key question related to our findings is how synonymous changes cause vastly different phenotypes. It is often assumed in endosymbiont transgenic experiments that synonymous changes do not affect function, and codons for the same amino acid are functionally redundant.

However, several decades of research have uncovered mounting evidence that the functional redundancy hypothesis is not always accurate Plotkin and Kudla, Indeed, codon bias varies across species hence the need for codon optimization Sharp et al.

When there is a rare codon, there are fewer tRNAs available, and the translation rate may be correspondingly lowered Li et al. Notably, there is a bias for rare codons in N-terminal regions of bacterial genes, which is likely due to their influence on mRNA structure Goodman et al. There is also evidence for selection across the domains of life on codon usage in the first 30—60 nucleotides of a gene, likely due to their impact on mRNA structure near the site of initiation Gu et al.

Additionally, early codons with low frequencies can be beneficial by slowing elongation, thus preventing ribosome collisions, or potentially helping to recruit chaperones to the emerging peptide for proper folding Tuller et al. In addition, some specific codons across the sequence are favored for their lower likelihood of mistranslation Qin et al. Indeed, synonymous codon differences in genes can result, for instance, in altered gene expression Kudla et al.

Beyond codon sequence, gene expression levels across representatives of all phenotypes are similar Figure 2 , supporting post-transcriptional differences as the source of functional variation and refuting the functional redundancy hypothesis in this context.

Examining the three synonymous transgenes more closely, we find that codon usage indeed correlates with phenotype. According to the codon usage table for D. In contrast, the w Suzi codon TCC has a frequency of Therefore, there is a pattern where the more common codons are present in the all-killing transgenes, and at similar frequencies, while the strain expressing partial male killing has a codon at a lower frequency. Thus, some of the phenomena described in studies on synonymous codons and function likely underlie the phenotypic variation observed here.

Given that transgenic wmk expression with silent site changes results in different phenotypes, we used software that estimated RNA secondary structures for the tested transgenes. Indeed, we found that the predicted structures correlate somewhat with phenotype, so synonymous codons may change transcript structure or other post-transcriptional features of the transcript or protein Figure 2.

Other small changes including adding a 9-codon N-terminal sequence ablated the phenotype and reversed it to no death, despite the additional sequence being smaller than many common gene tags that typically do not interfere with function.

This small change also alters predicted RNA structure Figure 2—figure supplement 1. We were unable to assess protein levels to determine translational differences due to lack of an antibody. However, the data demonstrates that the wmk transgene is functionally sensitive to some post-transcriptional changes, at least in the N-terminal region.

What, then, do these results illuminate about transgenic wmk function and the utility of transgenic research in light of the potential for marked influences of synonymous changes on phenotype?

First, as discussed, the wmk transgenic phenotypes are likely sensitive to post-transcriptional processes. Second, since three different transgenic phenotypes sex ratio bias, all killing, no killing have been found so far with only a few sequences analyzed, it is possible that further testing of new codons may increase the partial male killing to a full male-killing phenotype.

Therefore, it would be fruitful and may be possible to continue to uncover a transgenic sequence that fully recapitulates the phenotype to refine this system as a study tool for Wolbachia male killing. Third and critically, although the results show that there is some relationship between synonymous codons and phenotype, several points remain for further testing.

For example, we cannot conclude that the particular codon tested here is responsible for phenotype alterations in other host genetic backgrounds or species. It is possible that this codon plays a functional role only in a singular host genetic context. Here, we changed wmk sequences while holding the host genetic background fixed, but the reverse is required to conclude whether or not the particular codon plays a general role in other genotypes or natural contexts.

Second, due to possible coevolution, various codons may or may not yield similar functional effects across different host backgrounds, and additional synonymous sites may contribute to the male-killing phenotype.

Thus, the results here illuminate a previously unrecognized need for future research on the functional impacts of synonymous substitutions in endosymbionts. Future work may focus on determining if there is one specific synonymous codon that affects the male-killing function in all cases, if a more general feature exists where alteration of any or a subset of N-terminal or other wmk codons affects function, or if the effect of synonymous changes is specific to this background.

In addition, these findings are informative with regards to the more general study of phenotypes induced by endosymbiont transgenes. It is standard practice to codon optimize genes for maximizing host expression when testing endosymbiont gene function Perlmutter et al. However, if codon optimization potentially changes the interpretation of transgenic findings, then phenotypes should be corroborated in natural contexts once tools such as genetic editing are available in the relevant organism.

Specifically, wmk should be knocked out in native contexts once it is more technically feasible. In addition, codon optimization algorithms are updated with new information periodically with the assumption that they yield improved results, although it is unclear in practice whether an algorithm is better optimized to produce results that reflect the true biology of a transgene.

Future work is necessary to explore these concepts further. For example, comparisons of alleles may need to be performed with alleles identical in sequence except for any engineered differences, and the algorithm should remain constant across all transgenes that are compared to each other.

Further, careful analysis and comparison of transgenic phenotypes produced by different algorithms may be necessary in some cases where the phenotype is known or expected.

This approach could ensure the algorithm produces a transgenic phenotype that most closely resembles the natural phenotype. These principles are particularly important for research on endosymbionts that increasingly relies on heterologous gene expression for functional studies.

The smaller genomes of endosymbionts tend to have low GC content overall McCutcheon et al. We also find interesting support for the hypothesis that there is co-adaptation between wmk homologs and their hosts since male-killing genes may be evolutionary matched for the host sex determination and molecular machinery that they manipulate.

They did not recapitulate male killing when transgenically expressed in D. The lack of a sex ratio bias is expected if either wmk does not underpin male killing in these systems or divergent homologs are required to be closely co-adapted to their hosts.

This latter hypothesis is based on observations that resistance to male killing is common evidence of a potential host-microbe arms race Charlat et al. It is possible, then, that the wmk homologs tested induce a sex ratio bias in their natural hosts, but not in D.

If we make the assumption that wmk is involved in male killing in nature, which requires confirmation beyond transgenic recapitulation of the phenotype, then the results here give the basis for additional hypotheses that require further testing. First, the killing of both males and females by the closely-related w Rec and w Suzi wmk homologs could indicate that the target is something shared in both males and females, but functions differently within each sex assuming no off-target effects.

Indeed, previous studies on wmk and Wolbachia male killing demonstrate a positive correlation with between dosage compensation complex DCC activity and DNA defects in male embryos Riparbelli et al. Notably, four of the five protein components of the DCC are expressed in both males and females, and it is only msl-2 that is male-specific and catalyzes formation of the complex Lucchesi and Kuroda, Thus, there are several non-sex-specific wmk target candidates that the gene product may interact with to cause lethality in males and females.

Second, protein divergence and resultant conformational changes may impact the specificity between host target and the Wmk toxin, and could underlie development of host resistance. As such, major or minor divergence in protein or transcript sequence of either the host target or the microbial toxin may underpin changes that lead to common host resistance, wmk -host coadaptation, and functional transgene differences in the foreign D.

Third, these findings leave open the possibility of a variety of functionally relevant wmk protein or transcript conformations in nature, which could contribute to the marked diversity of Wolbachia male killing in terms of host species and sex determination systems Hurst et al.

The initial focus on trauma and resilience has now extended to work in substance use and NeuroHIV. Her research focuses on child lung health including HIV-associated lung disease, childhood pneumonia and childhood TB. In she received the World Lung Health Award, awarded by the American Thoracic Society at a ceremony in San Diego, in recognition of work that has "the potential to eliminate gender, racial, ethnic, or economic health disparities worldwide".

Currently regarded as a thought leader in Rheumatic Heart Disease, both on the continent and internationally. Has significant international research collaborations within the Rheumatic Heart Disease Community and within the Cardiovascular Community. Her social responsibility is reflected in, amongst others, numerous board positions; and she continues to be involved in teaching, training and mentorship encompassing courses directed at nurse practitioners, clinical officers and echocardiography masterclasses in South Africa, Ethiopia, Zambia and Uganda.