maggishaggy's Stars
github/personal-website
Code that'll help you kickstart a personal website that showcases your work as a software developer.
aertslab/pySCENIC
pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.
aertslab/SCENIC
SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.
MathiasHarrer/Doing-Meta-Analysis-in-R
[Read-Only] All R code and source files for the online guide "Doing Meta-Analysis with R: A Hands-On Guide"
FerRacimo/GRoSS
Graph-aware Retrieval of Selective Sweeps
immunogenomics/amp_phase1_ra
:ear_of_rice: Zhang, et al, Nature Immunology, 2019. Use single-cell transcriptomics and proteomics to study autoimmune diseases.
mrmckain/PhyDS
Phylogenetic iDentification of Subgenomes
Bribak/SURFY2
This repository constitutes SURFY2 and corresponds to the bioRxiv preprint 'Updating the in silico human surfaceome with meta-ensemble learning and feature engineering' by Daniel Bojar. SURFY2 is a machine learning classifier to predict whether a human transmembrane protein is located at the surface of a cell (the plasma membrane) or in one of the intracellular membranes based on the sequence characteristics of the protein. Making use of the data described in the recent publication from Bausch-Fluck et al. (https://doi.org/10.1073/pnas.1808790115), SURFY2 considerably improves on their reported classifier SURFY in terms of accuracy (95.5%), precision (94.3%), recall (97.6%) and area under ROC curve (0.954) when using a test set never seen by the classifier before. SURFY2 consists of a layer of 12 base estimators generating 24 new engineered features (class probabilities for both classes) which are appended to the original 253 features. Then, a soft voting classifier with three optimized base estimators (Random Forest, Gradient Boosting and Logistic Regression) and optimized voting weights is trained on this expanded dataset, resulting in the final prediction. The motivation of SURFY2 is to provide an updated and better version of the in silico human surfaceome to facilitate research and drug development on human surface-exposed transmembrane proteins. Additionally, SURFY2 enabled insights into biological properties of these proteins and generated several new hypotheses / ideas for experiments. The workflow is as following: 1) dataPrep Gets training data from data.xlsx, labels it according to surface class and outputs 'train_data.csv' 2) split Gets train_data.csv, splits it into training, validation and test data and outputs 'train.csv', 'val.csv', 'test.csv'. 3) main_val Was used for optimizing hyperparameters of base estimators and estimators & weights of voting classifier. Stores all estimators. Evaluates meta-ensemble classifier SURFY2 on validation set. 4) classifier_selection All base estimators and meta-ensemble approaches are tested on the initial dataset as well as the expanded dataset including the engineered features and compared in terms of their cross-validation score. 5) main_test Evaluates SURFY2 on the separate test set (trained on training + validation set). 6) testing_SURFY Evaluates the original SURFY through cross-validation and on validation as well as test set. 7) pred_unlabeled Uses SURFY2 to predict the surface label (+ prediction score) for unlabeled proteins in data.xlsx. Also gets the feature importances of the voting classifier estimators. 8) getting_discrepancies Compare predictions with those made by SURFY ('surfy.xlsx') and store mismatches. Also store the 10 most confident mismatches (by SURFY2 classification score) from each class. 9) feature_importances Plot the 10 most important features for the voting classifier estimators (Random Forest, Gradient Boosting, Logistic Regression) to interpret predictions. 10) base_estimator_importances Plot the 10 most important features for the two most important base estimators (XGBClassifier and Gradient Boosting). 11) comparing_mismatches Separate datasets into shared & discrepant predictions (between SURFY and SURFY2). Compare feature means and select features with the highest class feature mean differences between prediction datasets. Statistically analyze differences in features means between classes in both prediction datasets. Plot 9 representative features with their means grouped according to class and prediction dataset to rationalize discrepant predictions. 12) tSNE_surfy2 Perform nonlinear dimensionality reduction using t-SNE on proteins with predictions from both SURFY and SURFY2. Plot the two t-SNE dimensions and label the proteins according to their prediction class in order to see where discrepant predictions reside in the landscape. Plot surface proteins with most prevalent annotated functional subclasses and label them according to their subclass to enable comparison to class predictions. Functional annotations came from 'surfy.xlsx'.
1heidi/nar
This repo supports: Imker, H.J. (2018) “25 Years of Molecular Biology Databases: A Study of Proliferation, Impact, and Maintenance.” Frontiers in Research Metrics and Analytics 3.
juanu/CompMicroGenom
ODiogoSilva/Silva2014_RustPhylogenomics
The set of scripts used in Silva et al. 2014 "Genomic patterns of positive selection at the origin of rust fungi"
rcsb/review-NAR-Databases
Scripts to search database publication at Nucleic Acid Research for PDB keywords
juanu/ANI_analysis
juanu/MicroCompGenomics
kbradwell/bioinformatics-scripts
Scripts developed during genomic analysis of a eukaryotic parasite.
mrmckain/Secondary_Metabolite_Clustering
Scripts used for identifying gene clusters from target gene families in sequenced and annotated genomes.
nklimpert/mht_selection
Scripts for detecting changes in selective pressures on mycoheterotroph plastid genes using paml
othomantegazza/crossr
Compare transcriptomes from different species.
ecogene/EcoGene3.0
EcoGene 3.0 is a new web interface for EcoGene.org built using the Drupal open source website software. EcoGene 3.0 both present data from our daily-updated MySql relational database. Please cite the following reference for this resource: Jindan Zhou and Kenneth E. Rudd (2013) EcoGene 3.0 Nucleic Acids Research, 41 (D1): D613-D624.
genomeannotation/HamstrFiltr
Start with a genome, single copy orthologs and SNPs; end with a list of the SNPpiest single copy ortholog exons in the genome
juanu/ThesisChapter_MetagenomeSNPs
A repository of some of the Ipython notebooks used on one of my Thesis chapters.
kbradwell/comparative_trypanosoma_paper
Custom scripts for recreating some of the analyses from the trypanosoma genomic comparisons work.
maggishaggy/merlin-p
A prior-based integrative framework for functional transcriptional regulatory network inference (Fotuhi Siahpirani & Roy, Nucleic Acids Research 2017)
maggishaggy/personal-website
Code that'll help you kickstart a personal website that showcases your work as a software developer.
maggishaggy/SequenceClusterScripts
A set of scripts to analyze the output of a clustering program, such as orthoMCL.In their current version, most of this scripts are adapted to our local environment, and using JGI genome annotation
maggishaggy/SingleSplice
Algorithm for detecting alternative splicing in a population of single cells. See details in Welch et al., Nucleic Acids Research 2016: http://nar.oxfordjournals.org/content/early/2016/01/05/nar.gkv1525.full
morningjasimine/ortholog
This project is develop for single copy orthologs detection based on colinear of reference genome.
nyudanin/pancanatlas_code_public
Public repository containing research code for the TCGA PanCanAtlas Splicing project
qiao-xin/free-photos
Places to find CC0 photos and the like
trinker/ggtree-1
This is a read-only mirror of the Bioconductor SVN repository. Package Homepage: http://bioconductor.org/packages/devel/bioc/html/ggtree.html Contributions: https://github.com/GuangchuangYu/ggtree. Bug Reports: https://support.bioconductor.org/p/new/post/?tag_val=ggtree or https://github.com/GuangchuangYu/ggtree/issues.