refactoring

.gitignore

@@ -1,3 +1,4 @@
 mkd_venv
 .idea
-.project
+.project
+.cache/

docs/software.md

@@ -8,11 +8,14 @@ software or computational resources of the lab.
 
 The Human Phenotype Ontology (HPO) aims to provide a standardized vocabulary of phenotypic abnormalities encountered in human disease.
 Terms in the HPO describe individual phenotypic abnormalities such as atrial septal defect.
-For further details and information please refer to the 
-[Human Phenotype Ontology Homepage](http://www.human-phenotype-ontology.org). The HPO is developed 
-as a part of the [Monarch Initiative](http://monarchinitiative.org).
+The HPO is developed as a part of the [Monarch Initiative](http://monarchinitiative.org).
+S|HPO - Homepage||link:http://www.human-phenotype-ontology.org|
 S|HPO - GitHub||link:https://github.com/obophenotype/human-phenotype-ontology|
 
+## The Medical Action Ontology (MAxO)
+the Medical Action Ontology (MAxO), the first ontology specifically designed to organize medical procedures, therapies, and interventions in a structured way. 
+Currently, MAxO contains 1757 medical action terms added through a combination of manual and semi-automated processes. 
+S|MAxO - GitHub||link:https://github.com/monarch-initiative/maxo|
 
 ## The Global Alliance for Health (GA4GH) Phenopacket Schema
 
@@ -33,9 +36,9 @@ S|phenopacket-tools - GitHub||link:https://github.com/phenopackets/phenopacket-t
 
 The Exomiser is a Java program that functionally annotates and prioritises variants from whole-exome sequencing data starting from a 
 VCF file. The Exomiser was developed by our group, Damian Smedley and Jules Jacobsen of the Mouse Informatics Group at the Sanger Institute (now at the 100,000 Genomes Project and Quenn Mary's University in London), and other members of the [Monarch Initiative](http://monarchinitiative.org).
+
 - [Robinson et al., 2014, Genome Research](https://pubmed.ncbi.nlm.nih.gov/24162188/){:target="_blank"} and [Smedley et al. (2015)](http://www.ncbi.nlm.nih.gov/pubmed/26562621){:target="_blank"}
 S|Exomiser - GitHub||link:https://github.com/exomiser/Exomiser|
-
 - An [online demo version](https://exomiser.monarchinitiative.org/exomiser/) is available
 
 
@@ -51,9 +54,57 @@ The software then ranks all diseases from OMIM, Orphanet, and DECIPHER by a scor
 - See also [Kohler, S. et al., 2009](http://www.ncbi.nlm.nih.gov/pubmed/19800049)
 
 
+## Phenol
+Phenol is a Java library for working with JSON phenotype ontologies including especially the Human Phenotype Ontology and 
+the Mammalian Phenotype Ontology and associate phenotype annotation files.
+S|Phenol - GitHub||link:https://github.com/monarch-initiative/phenol|
+
+## LIRICAL
+LIkelihood Ratio Interpretation of Clinical AbnormaLities (LIRICAL)   exploits the likelihood ratio (LR) 
+framework to provide an estimate of (1) the posttest probability of candidate diagnoses, 
+(2) the LR for each observed HPO phenotype, and (3) the predicted pathogenicity of observed genotypes.
+S|LIRICAL - GitHub||link:https://github.com/TheJacksonLaboratory/LIRICAL|
+
+
+<figure markdown>
+![GA4GH Phenopacket Schema](img/lirical.jpg){ width="400" }
+<figcaption>Robinson PN, et al. (2020) Interpretable Clinical Genomics with a Likelihood Ratio Paradigm. Am J Hum Genet; 107:403-417.
+</figcaption>
+</figure>
+
+## SQUIRLS
+The Super Quick Information-content Random-forest Learning of Splice variants (SQUIRLS) algorithm 
+generates a small set of interpretable features for machine learning by calculating the information-content 
+of wild-type and variant sequences of canonical and cryptic splice sites, assessing changes in candidate 
+splicing regulatory sequences, and incorporating characteristics of the sequence such as exon length, 
+disruptions of the AG exclusion zone, and conservation. 
+S|SQUIRLS - GitHub||link:https://github.com/TheJacksonLaboratory/SQUIRLS|
+
+<figure markdown>
+![SQUIRLS](img/squirls.jpg){ width="400" }
+<figcaption>Danis D, et al (2021) Interpretable prioritization of splice variants in diagnostic next-generation sequencing. 
+Am J Hum Genet; 108:1564-1577.
+</figcaption>
+</figure>
+
+
+## SvAnna
+Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically 
+underascertained owing to sequencing technology limitations. Long-read sequencing enables comprehensive 
+detection of SVs, but approaches for prioritization of candidate SVs are needed. Structural variant 
+Annotation and analysis (SvAnna) assesses all classes of SVs and their intersection with transcripts and regulatory 
+sequences, relating predicted effects on gene function with clinical phenotype data. 
+S|SvAnna - GitHub||link:https://github.com/TheJacksonLaboratory/Svanna|
+
+<figure markdown>
+![SvAnna](img/svanna.png){ width="400" }
+<figcaption>Danis D. et al. (2022) SvAnna: efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing. 
+Genome Med; 14:44. 
+</figcaption>
+</figure>	
+
 
 
-			
 ## ChIP-seq software
 
 Chromatin immunoprecipitation coupled with next-generation sequencing (ChIP-seq) is a powerful technology to identify the genome-wide locations of transcription factors and other DNA binding proteins. Computational ChIP-seq peak calling infers the location of protein-DNA interactions based on various measures of enrichment of sequence reads.
@@ -64,22 +115,41 @@ analysis of ChIP-seq data for reproducible identification of binding peaks. Geno
 S|Q - GitHub||link:https://github.com/charite/Q|
 * [Hansen P, et al. (2016) Q-nexus: a comprehensive and efficient analysis pipeline designed for ChIP-nexus. BMC Genomics 17:873](https://pubmed.ncbi.nlm.nih.gov/27814676/)
 
+## GOPHER
+ Target enrichment combined with chromosome conformation capturing methodologies such as capture Hi-C (CHC) can be used 
+ to investigate spatial layouts of genomic regions with high resolution and at scalable costs. A common application of CHC is the investigation of regulatory elements that are in contact with promoters, but CHC can be used for a range of other applications. 
+GOPHER (Generator Of Probes for capture Hi-C Experiments at high Resolution) is an easy-to-use and robust desktop application for CHC probe design
+S|GOPHER - GitHub||link:https://github.com/TheJacksonLaboratory/Gopher|
 
+<figure markdown>
+![HBA-DEALS](img/gopher.png){ width="400" }
+<figcaption>Hansen P et al. (2019) GOPHER: Generator Of Probes for capture Hi-C Experiments at high Resolution. BMC Genomics; 20(1):40.</figcaption>
+</figure>
 
+## HBA-DEALS
+Hierarchical Bayesian Analysis of Differential Expression and ALternative Splicing (HBA-DEALS) is an R package that 
+simultaneously characterizes differential gene expression and alternative splicing in high-throughput gene expression data. 
+It uses counts data for isoforms (alternative transcripts of a gene) to infer the parameters of a hierarchical 
+Bayesian model of expression and splicing. It then uses the posterior of the parameters to determine the existence 
+of differential expression and/or differential alternative splicing. Isoform counts can be derived from short-read 
+sequencing RNA-Seq data or from long-read RNA sequencing data such as that generated by the PacBio SequelII platform.
+S|HBA-DEALS - GitHub||link:https://github.com/TheJacksonLaboratory/HBA-DEALS|
 
-## IMSEQ--a fast and error aware approach to immunogenetic sequence analysis
-
-Recombined T- and B-cell receptor repertoires are increasingly being studied using next generation sequencing (NGS) in order to interrogate the repertoire composition as well as changes in the distribution of receptor clones under different physiological and disease states. 
-This type of analysis requires efficient and unambiguous clonotype assignment to a large number of NGS read sequences, 
-including the identification of the incorporated V and J gene segments and the CDR3 sequence. 
-Current tools have deficits with respect to performance, accuracy and documentation of their underlying algorithms and usage.
-IMSEQ is a method to derive clonotype repertoires from NGS data with sophisticated routines for handling errors stemming 
-from PCR and sequencing artefacts. The application can handle different kinds of input data originating from single- or 
-paired-end sequencing in different configurations and is generic regarding the species and gene of interest.
-The software can be downloaded from the [project homepage](https://www.imtools.org).
-
-- See also [Kuchenbecker, L. et al., 2015](http://www.ncbi.nlm.nih.gov/pubmed/25987567).
-
-
+<figure markdown>
+![HBA-DEALS](img/hbadeals.png){ width="400" }
+<figcaption>Karlebach G, et al. (2020) HBA-DEALS: accurate and simultaneous identification of differential expression and splicing using hierarchical Bayesian analysis. Genome Biol;21:171. 
+</figcaption>
+</figure>
      
+## isopret
+isopret (*isoform interpretation*) is a method that uses expectation–maximization to infer isoform-specific 
+functions based on the relationship between sequence and functional isoform similarity. 
+We predicted isoform-specific functional annotations for 85 617 isoforms of 17 900 protein-coding human genes spanning 
+a range of 17 430 distinct gene ontology terms. 
+S|isopretEM - GitHub||link:https://github.com/TheJacksonLaboratory/isopretEM|
+
+<figure markdown>
+![HBA-DEALS](img/isopret.jpg){ width="400" }
+<figcaption>Karlebach G (2023) An expectation-maximization framework for comprehensive prediction of isoform-specific functions. Bioinformatics; 39(4):btad132.</figcaption>
+</figure>