IMAGE ( Iterative Mapping and Assembly for Gap Elimination) is a software designed to close gaps in any draft assembly using Illumina paired end reads. IMAGE is best described in several stages: aligning of Illumina reads at contig ends; local assembly of reads into new contigs; reference contigs are extended or merged; iterating the whole process to extend and merge more contigs.
FIATAS is used for assay annotation . The annotation tool has been developed with the idea that the editor/annotators are presented with the set of images associated with a single assay to be annotated, and an optimized anatomy ontology so the expert user can rapidly assign annotations to a given image set. Each image in the set can be independently annotated in terms of tissues/structures that show expression and the nature, strength and patterning of that expression. Comments can be added to any annotation as well as a general comment on the whole set.
Img2net is a software to automatedly analyze such structures by reconstructing the underlying network, computing relevant network properties, and statistically comparing networks of different types or under different conditions.
CFNet combines a novel rotation equivariant convolution scheme, called conic convolution, and the DFT to aid networks in learning rotation-invariant tasks. This network has been especially designed to improve performance of CNNs on automated computational tasks related to microscopy image analysis.
SV-plaudit provides a pipeline for creating image views of genomic intervals, automatically storing them in the cloud, deploying a website to view/score them, and retrieving scores for analysis. SV-plaudit supports image generation sequencing data from BAM or CRAM files from Illumina paired-end sequencing, PacBio or Oxford Nanopore Technologies long-read sequencing, or 10X Genomics linked-read sequencing.
CoGI is a novel approach for genome compression, which transforms the genomic sequences to a two-dimensional binary image (or bitmap), then applies a rectangular partition coding algorithm to compress the binary image.
Frida (FRamework for Image Dataset Analysis) is image analysis software. Frida was developed by the Johns Hopkins University Tissue Microarray Core Facility. Frida is open source and written in 100% Java. Frida makes use of functionality from the NIH’s ImageJ application.
Cornish T, Morgan J, Gurel B, and De Marzo AM.
FrIDA: An open source framework for image dataset analysis.
Arch. Pathol. Lab. Med. 132:856 (2008). Originally presented at Advancing Practice, Instruction and Innovation Through Informatics: Pittsburgh, PA, 2007.