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167 results for “coding sequences”
YaCoS: a Complete Infrastructure to the Design and Exploration of Code Optimization Sequences
<p>The growing popularity of machine learning frameworks and algorithms has greatly contributed to the design and exploration of good code optimization sequences. Yet, in spite of this progress, mainstream compilers still provide users with only a handful of fixed optimization sequences. Finding optimization sequences that are good in general is challenging because the universe of possible sequences is potentially infinite. This paper describes a infrastructure that provides developers with the means to explore this space. Said infrastructure, henceforth called YaCoS, consists of benchmarks,search algorithms, metrics to estimate the distance between programs, and compilation strategies. YaCoS’s features let users build learning models that predict, for unknown pro-grams, optimization sequences that are likely to yield good results for them. In this paper, as a case study, we have used YaCoS to find good optimization sequences for LLVM, using code size as the objective function. Such study lets us evaluate three feature sets: two variations of the feature vectors proposed by Namolaru at al in 2010, plus the optimization statistics produced by LLVM. Our results show that YaCoSis able to find sequences that improve onto clang -Oz by3.75% on average. Our experiments do not indicate a dominant feature set out of the three approaches that we have investigated—it is possible to find programs in which one of them is strictly better than the others.</p>
Genomics polymorphisms of Staphylococcus aureus strain NCTC 8325 in the lab stock maintained at TUM (WT), after 30 passes in BHI media (D) and after 30 passes detecting 4 -fold MIC increase to isocyanide -code I16- 3 biological replicates (A,B,C), and 3 independent colonies sequenced per replicate at the end of the experiment.
<p>Genomics polymorphisms of Staphylococcus aureus strain NCTC 8325 in the lab stock maintained at TUM (WT), after 30 passes in BHI media (D) and after 30 passes detecting 4 -fold MIC increase to isocyanide -code I16- 3 biological replicates (A,B,C), and 3 independent colonies sequenced per replicate at the end of the experiment. Determined from Illumina shotgun genomic sequencing datasets, mapping and analyses vs the reference genome of the strain https://www.ncbi.nlm.nih.gov/nuccore/NC_007795.1/</p>
Associated code and data for "A Practical Guideline for MicroRNA Sequencing Data Analysis in Chronic Lymphocytic Leukemia (doi: 10.1007/978-1-0716-4290-0_18)".
<p>This deposit contains the data, code, and analysis to recreate the results in the manuscript - Tuulikki Suomela, Liang Zhang, Julio Vera, Heiko Bruns, Xin Lai. A Practical Guideline for MicroRNA Sequencing Data Analysis in Chronic Lymphocytic Leukemia. Methods Mol. Biol., 2883, 403–426. <a href="https://www.researchgate.net/publication/387267721_A_Practical_Guideline_for_MicroRNA_Sequencing_Data_Analysis_in_Chronic_Lymphocytic_Leukemia">https://doi.org/10.1007/978-1-0716-4290-0_18</a>.</p> <p>The pipeline allows users to perform end-to-end analysis of bulk miRNA sequencing data, including quality control of FastQ files, mapping of read counts to miRNA genes using miRBase or Reference genome, quantification of miRNA read counts, differential gene expression analysis using DEseq2, gene set enrichment analysis using curated cancer hallmark gene sets, and identification of miRNA targets.</p> <p>If you have used the code for your research, please cite the original publication. Thank you very much.</p>
Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device"
<p><strong>Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device".</strong></p> <pre>Code to analyze data produced by the Quantum-Si benchtop device and semiconductor chip is provided in a Python library <strong>qsi_algo</strong> under several submodules: - <strong>rs_caller.py</strong>: Algorithm for calling RS segments (also called ROI segments throughout code). - <strong>rs_caller_controller.py</strong>: Code framework for executing RS calling and property computation in a distributed manner - <strong>rs_properties</strong>: Code for computing properties of identified RS - <strong>rs_classifier</strong>: Algorithms for identifying peptide states (i.e. residue calls) associated with an RS - <strong>utils.py</strong>: shared helper code - <strong>pulse_reader</strong>: reader for binary pulse file - <strong>filters</strong>: ROI and pulse filtering utilities - <strong>plotting</strong>: functions for visualization of data relevant to the analyses presented Jupyter notebooks (<strong>.ipynb</strong>) files are named according to the manuscript figure they are associated with. Analysis code inside uses provided RS (recognition segment) data to demonstrate filtering and residue-calling techniques required to replicate analyses shown in manuscript figures. Please note: several methods rely on randomization for model initialization and/or data sampling which can cause small deviations from equivalent analyses in published figures. The raw data produced from the Quantum-Si benchtop device and semiconductor chip for the assays presented in the accompanying study is presented in a pulse-called binary file format. Pulses can be used as input for RS identification and peptide state identification. Pre-segmented (RS-identified) files are included for convenience. The data contained in the files include: <strong>{run_id}.bin</strong>: Binary format for storing pulse info. The reader provided in <strong>qsi_algo.pulse_reader</strong> produces the following columns: - <strong>aperture_index</strong>: unique aperture index on chip - <strong>start_f</strong>: index of first frame in pulse, counted from the beginning of the run - <strong>end_f</strong>: index of last frame in pulse, counted from the beginning of the run - <strong>dur_f</strong>: duration of pulse in frames - <strong>dur_s</strong>: duration of pulse in seconds - <strong>ipd_f</strong>: interpulse duration in frames (number of frames since end of preceding pulse) - <strong>ipd_s</strong>: interpulse duration in seconds (time in seconds elapsed since end of preceding pulse) - <strong>snr</strong>: signal-to-noise ratio (bin1_intensity / bin1_bg_std) - <strong>intensity</strong>: intensity of pulse (counts above baseline in bin1) - <strong>bin0_intensity</strong>: counts above baseline in bin0 - <strong>intensity_display</strong>: bin1_intensity + bin1_bg_mean - <strong>binratio</strong>: bin0_intensity / bin1_intensity - <strong>bg_mean</strong>: bin1 background mean in region of pulse - <strong>bg_std</strong>: bin1 background standard deviation in region pulse - <strong>bin0_bg_mean</strong>: bin0 background mean in region of pulse - <strong>bin0_bg_std</strong>: bin0 background standard deviation in region pulse <strong>{run_id}.csv.gz</strong>: Compressed comma-separated value file containing RS/ROI properties computed from raw pulses.bin file by included RS caller (example in <strong>rs_caller.py</strong>). - <strong>ap</strong>: unique aperture index on chip - <strong>ROI</strong>: ordinal ROI number in the aperture, 0-indexed - <strong>start_p</strong>: index (.loc) of first pulse in the ROI (inclusive) in pulse dataframe - <strong>end_p</strong>: index (.loc) of last pulse in the ROI (inclusive) in pulse dataframe - <strong>start_f</strong>: first frame of the first pulse in the ROI (inclusive) - <strong>end_f</strong>: Last frame of the last pulse in the ROI (exclusive) - <strong>start_s</strong>: Time (in seconds elapsed from beginning of run) of the start of the ROI - <strong>end_s</strong>: Time (in seconds elapsed from beginning of run) of the end of the ROI - <strong>dur_f</strong>: Duration in frames of the ROI - <strong>dur_s</strong>: Duration in seconds of the ROI - <strong>num_pulses</strong>: Number of pulses in the ROI (that also passed filtering during ROI-calling) - <strong>pw_mean</strong>: Mean pulse duration (in seconds) of pulses in the ROI - <strong>ipd_mean</strong>: Mean inter-pulse duration (in seconds) of pulses in the ROI - <strong>snr_mean</strong>: Mean signal-to-noise ratio of pulses in the ROI - <strong>intensity_mean</strong>: Mean intensity above baseline of pulses in the ROI - <strong>binratio_norm</strong>: Estimated pulse bin ratio of pulses in the ROI, according to the following equation: sum(bin0_intensity*dur_f) / np.sum(bin1_intensity*dur_f) - <strong>ROI_score</strong>: ROI quality score (0-1 from least to most likely to contain recognizer-peptide recognition pulsing) - <strong>binratio_skew</strong>: bin ratio correction factor accounting for binning signal timing differences across the chip. This factor has already been applied to the binratio_norm column</pre>
Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.
<p>*.sh and *.R are codes to test rare coding variants for association with ISCAD.</p> <p>Petrazzini_etal_2024_*_level_meta_analysis.txt.gz are summary statistics of variant- and gene-level associations of rare coding variants in the exome sequences of 604,914 individuals with an in-silico score for coronary artery disease (ISCAD).</p> <p>Chromosomal positions are mapped to the GRCh38 (hg38) human genome reference.</p> <p>Directions of effect correspond to associations in the UK Biobank, the All of Us Research Program, the BioMe Biobank sample 1 and the BioMe Biobank sample 2, in that order.</p>
Experimental data for "An End-to-End Coding Scheme for DNA-Based Data Storage With Nanopore Sequenced Reads"
<p>The experimental dataset used in "An End-to-End Coding Scheme for DNA-Based Data Storage With Nanopore Sequenced Reads."</p> <p>A set of 91,766 150-nt oligos were synthesised with GenScript (oligos.fasta). Each oligo consists of a pseudo-random 110-nt payload flanked by 20-nt primers at each end. The strands are split in three roughly equal groups (two groups of 30,589 and one group of 30,588). Each group has a dedicated primer pair for targeted PCR amplification (the primer pairs used for amplification are provided in primers_synthesis.fasta). The pseudo-random payload was designed to avoid primer-payload collisions.</p> <p>For each file, a sample from the synthesised pool was PCR amplified using the corresponding primer pair and sequenced using Oxford Nanopore Technologies MinION sequencing device following the standard library preparation protocol for amplicon DNA. The raw reads were basecalled using guppy, either in fast- ("acc-false") or high-accuracy ("acc-true") regime. The basecaller generated two groups of reads—"passQ-true" for the reads that passed the quality-score threshold of 8 and "passQ-false" for those that did not. For each group of reads, a BLAST-based fuzzy search for primer sequences was performed and, based on the resulting alignments, the segments containing the correct primer pairs and located at a distance of 150+-15nt were extracted (separately for forward and reverse-complemented reads). The segments are then assigned to the closest synthesized strand based on Levenshtein distance. The resulting clusters are used to estimate the parameters of the end-to-end DNA storage channel model and to test the proposed error-correction scheme.</p> <p>The archive clustered_read_segments.tar.gz contains 12 sub-archives, for each file (0,1,2), accuracy ("acc-true" or "acc-false"), and Q-score ("passQ-true" or "passQ-false"). Within each sub-archive, there are two folders (one for forward read segments and one for backward read segments), and each folder contains two files: one for the reference synthesised (or "transmitted") sequences that correspond to the file in question ("TX__" — e.g., "TX__file=0_accBaCa=true_passQ=true_filter=true_forward_.txt") and another file for the sequenced (or "received") segment clusters ("RX__" — e.g., "RX__file=0_accBaCa=true_passQ=true_filter=true_forward_.txt"). The received clusters in the "RX__" file are ordered in correspondence with the synthesised sequences in the "TX__" file, and a line "===============================" is used as a separator.</p>
Source code of tables for the publication "K- and L-shell theoretical fluorescence yields for the Fe isonuclear sequence"
<p>In this work, we present K- and L- shell fluorescence yield values of the full isonuclear sequence of Fe ions, using a state-of-the-art multiconfiguration Dirac-Fock approach. These results may be of importance for spectral fitting and plasma modeling, both in laboratory and astrophysical studies, where Fe is an important benchmark element. The K-shell fluorescence yields were found to be very similar up to the removal of 14 electrons.</p>
Emergence and radiation of distemper viruses in terrestrial and marine mammals - Input files, bash and R codes for analysing PDV and CDV sequence data
<p><span>Canine distemper virus (CDV) and phocine distemper virus (PDV) are major pathogens to terrestrial and marine mammals. Yet little is known about the timing and geographical origin of distemper viruses and to what extent it was influenced by environmental change and human activities. To address this, we i) performed the first comprehensive time-calibrated phylogenetic analysis of the two distemper viruses; ii) mapped distemper antibody and virus detection data from marine mammals collected between 1972-2018; iii) and compiled historical reports on distemper dating back to the 18<sup>th</sup> century. We find that CDV and PDV diverged in the early 17<sup>th</sup> century. Modern CDV strains last shared a common ancestor in the 19<sup>th</sup> century with a marked radiation during the 1930s-50s. Modern PDV strains are of more recent origin, diverging in the 1970s-80s. Based on the compiled information on distemper distribution, the diverse host range of CDV and basal phylogenetic placement of terrestrial morbilliviruses, we hypothesize a terrestrial CDV-like ancestor giving rise to PDV in the North Atlantic. Moreover, given the estimated timing of distemper origin and radiation, we hypothesize a prominent role of environmental change such as the Little Ice Age, and human activities like globalisation and war in distemper virus evolution. </span></p>
Massive Chinese domestic pigs provide missing sequences in reference genome and reveal non-coding sequence variations regulating gene expression across Eurasian boars
<p>This dataset contains novel sequences in Chinese domestic pigs but is absent in Sscrofa 11.1 reference genome. The detailed information for each file is recorded in the README file.</p>
Emergence and radiation of distemper viruses in terrestrial and marine mammals - Input files, bash and R codes for analysing PDV and CDV sequence data
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Divergence in coding sequence and expression of different functional categories of immune genes between two wild rodent species
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Data from: A hypervariable mitochondrial protein coding sequence associated with geographical origin in a cosmopolitan bloom-forming alga, Heterosigma akashiwo
Geographic distributions of phytoplankton species can be defined by events on both evolutionary time and shorter scales, e.g., recent climate changes. Additionally, modern industrial activity, including the transport of live fish and spat for aquaculture and aquatic microorganisms in ship ballast water, may aid the spread of phytoplankton. Obtaining a reliable marker is key to gaining insight into the phylogeographic history of a species. Here, we report a hypervariable mitochondrial gene in the cosmopolitan bloom-forming alga, Heterosigma akashiwo. We compared the entire mitochondrial genome sequences of seven H. akashiwo strains from Japanese and North American coastal waters and identified a hypervariable segment. The region codes for a hypothetical protein with no defined function, and its variations between Japanese and North American isolates, were prominent, while the sequences were more conserved among Japanese strains and North American isolates. Comparison of the sequence in isolates obtained from different geographical points in the Northern Hemisphere revealed that the sequence variations largely correlated with latitude and longitude (i.e. Pacific/Atlantic oceans). Our results demonstrate the usefulness of the sequence in determining the phylogeographic history of H. akashiwo.
Integration of protein and coding sequences enables mutual augmentation of the language model
<p><strong>The file structure is as follows:</strong></p> <p>Project Root<br>├── TE_MRL<br>│ ├── MRL_dataset.zip<br>│ └── TE_dataset.zip<br>│<br>├── finetuned_model<br>│ ├── FoldP<br>│ ├── LocP<br>│ ├── SSP<br>│ └── SolP<br>│<br>├── tax_tsne<br>│ └── emb_3models.zip<br>│<br>└── training_data<br> ├── FoldP.csv<br> ├── LocP.csv<br> ├── SolP.csv<br> ├── SSP.pkl<br> └── pretrain_source_GCF.txt</p>
scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution - Supplementary data and code
<p>Data and code to reproduce the analyses from the study: "scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution". </p>
Inlist and Source Code Files for "Fossil Signatures of Main-sequence Convective Core Overshoot Estimated through Asteroseismic Analyses"
<p>MESA (r12778) and GYRE (version 6.0) inlist files used in the work described in "Fossil Signatures of Main-sequence Convective Core Overshoot Estimated through Asteroseismic Analyses".</p><p>Two subdirectories are provided in the archive:</p><p>1) The directory called "inlists" contains different MESA inlist files for different evolutionary period (pms=pre main sequence, ms=main sequence, and rgb=red giant branch) of the stellar model. The file named "inlist_0all" is applied to all evolutionary periods. The different inlist files for the different evolutionary states are called by putting their names in the "inlist" file, for example the include file named "inlist" evolves a MESA model from the pre main sequence until ZAMS (using the stop_near_zams = .true. option in the "inlist_1pms" file). An example gyre (version 6.0) inlist is also included in the "inlists" subdirectory. The Python scripts used to evaluate the matrix elements (which are used to determine the dipolar mixed-mode frequencies) discussed in this work are available at https://gitlab.com/darthoctopus/mesatricks. </p><p>2) Custom stopping conditions (used for stopping a model before the red giant branch) as well as a custom diffusion cutoff (see Viani et al. 2018, ApJ, 858, 28) are included in the run_star_extras.f file in the "src" subdirectory. </p>
A massive proteogenomic screen identifies thousands of novel human protein coding sequences
<p>Accurate annotation of genes in the human genome is fundamental for biomedical research and genomic data interpretation. The Ensembl, RefSeq, and GENCODE consortiums continuously update the human genome annotations based on new computational and experimental evidence, and new proteins were identified constantly. The Genotype-Tissue Expression (GTEx) project has generated more than 15,000 RNA sequencing dataset from multiple-tissues of more than 800 donors which allows to model almost all transcripts and proteins in the human genome. Using proteins translated from the GTEx transcript model, more than 21 million in-silico trypsin-digested peptides were generated. To identify high-confidence novel proteins with proteomic support, we screened more than 2,000 proteomic projects in the PRIDE database and selected more than 50,000 mass spectrometry (MS) runs from 923 projects. These MS data were used to validate the predicted novel peptides. With a stringent standard, we identified almost 20,000 novel peptides. </p> <p>This dataset include files used in the the above analysis. More details can be found in the GitHub page (https://github.com/ATPs/human_novo_protein_2022). </p>
RNA sequencing code associated with Billipp et al. Immunity 2024 - Chat + vs Chat - tuft cells
<p>R code and result for RNA-sequencing analysis comparing <em>Chat(GFP)+ </em>and <em>Chat(GFP)- </em>tuft cells sorted from the small intestine of <em>B6.Chat-GFP</em> mice. Associated with Billipp et al. Immunity 2024 titled "Tuft cell-derived acetylcholine promotes epithelial chloride secretion and intestinal helminth clearance"</p>
RNA sequencing code associated with Billipp et al. Immunity 2024 - Prox vs. distal tuft cells
<p>R code and result for RNA-sequencing analysis comparing tuft cells sorted from the proximal and distal small intestine of mice. Associated with Billipp et al. Immunity 2024 titled "<span>Tuft cell-derived acetylcholine promotes epithelial chloride secretion and intestinal helminth clearance</span>"</p>
Identification of a novel non-coding deletion in Allan-Herndon-Dudley syndrome by long-read HiFi genome sequencing
<p>These are the VCF files of structural variant (SV) calls for two sibling patients (II:2 [GMPB009_1] and II:3 [GMPB009_4]) generated by PacBio HiFi long-read genome sequencing.</p> <p>Sequence reads were processed using the <a href="https://github.com/PacificBiosciences/pb-human-wgs-workflow-snakemake">PacBio Human WGS workflow</a> with the human reference genome (hg38), and SVs were identified using '<a href="https://github.com/PacificBiosciences/svpack">svpack</a>'.</p>
Source data and code for manuscript 'An executive network for the control of sequence-behavior in pigeons'
<p>The contents of this folder are part of the submission of the manuscript entitled 'An executive network for the control of sequence-behavior in pigeons', by Lukas Alexander Hahn & Jonas Rose</p> <p>Contact: lukas.hahn@ruhr-uni-bochum.de</p> <p>Data and code have been compressed into a .zip folder each. Unpack the contents of the folders to use the dataset. The dataset is split into two main folders and one Matlab file:</p> <p>'code'<br> contains all analysis code to produce all figures and reported statistics of the manuscript (refer to the<br> MATLAB live script 'manuscriptResultsLiveScript.mlx' to run the analysis, please adjust the path information of where the data is stored on your computer).</p> <p>'RESULTSSTATISTICS.mat'<br> contains all reported statistical values (generated by 'manuscriptResultsLiveScript.mlx')</p> <p>'sourceData'<br> Contains all required source data files (i.e. pre-processed data) required to run the analyses stored in 'code'.</p> <p>Data related to animal behavior was recorded using MATLAB (R2016b). Electrophysiological data was recorded by NeuroNexus microelectrodes and an INTAN RHD2000 headstage on an INTAN USB-Interface board, with a sampling rate of 30 kHz and was subsequently filtered for spike sorting at bandpass 0.5 - 7.5 kHz.</p> <p>Data format is the MATLAB '.mat' type (which can be loaded in by MATLAB, or alternatively by the freely available Octave Software (https://www.gnu.org/software/octave)).<br> Data is organized in MATLAB structures, one file per session for behavioral results, one file per neuron for different alignments and preprocessing conditions (refer to manuscriptResultsLiveScript).<br> Structures contain individual matrices (labelled by a descriptive name) that contain numerical values or character strings.<br> Matrices labelled by the keyword 'Info' contain character strings that give a brief description of the loaded data.<br> Source data contains two separate folders containing data of animal 1 ('P855'), and animal 2 ('T1003').</p> <p>Data was sorted into different subsets, for analysis of individual task phases. Subfolder 'NCL' refers to 'nidopallium caudolaterale', 'NIML' refers to 'nidopallium intermedium mediale pars laterale', the recorded brain regions.</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.