SF^2HDL： A Computational Tool of State Transition Diagram Translation

The lack of standard to electronic circuits modeling made possible the development of many tools and modeling languages for electronic circuits. In this way, several tools to be used on different descriptions stage of the designs are necessary. This paper presents a tool called SF^2HDL （Stateflow to Hardware Description Language or State Transition Table） that translates a finite state machine on state transition diagram representation, described by Stateflow tool, into an input file standard for TABELA program or into a file behavioral VHDL （Very High Speed Integrated Circuits Hardware Description Language） directly. The TABELA program was used to optimization this finite state machine. After that, the TAB2VHDL program was used to generate the VHDL code on register transfer level, what permits comparisons with results obtained by synthesis. The finite state machine must be described by Mealy model and the user can describe the machine on high level abstraction using all Simulink supports. The tool was very efficient on computational cost and it made translation of several cases, for the two VHDL description models. Every state machine translated was simulated and implemented on device EP2C20F484C7 using Quartus II environment.

An introduction to computational tools for differential binding analysis with ChlP-seq data

Background： Gene transcription in eukaryotie cells is collectively controlled by a large panel of ehromatin associated proteins and ChIP-seq is now widely used to locate their binding sites along the whole genome. Inferring the differential binding sites of these proteins between biological conditions by comparing the corresponding ChIP-seq samples is of general interest, yet it is still a computationally challenging task. Results： Here, we briefly review the computationhl tools developed in recent years for differential binding analysis with ChIP-seq data. The methods are extensively classified by their strategy of statistical modeling and s＇cope of application. Finally, a decision tree is presented for choosing proper tools based on the specific dataset. Conclusions： Computational tools for differential binding analysis with ChIP-seq data vary significantly with respect to their applicability and performance. This review can serve as a practical guide for readers to select appropriate tools for their own datasets.

Computational Tools and Resources for CRISPR/Cas Genome Editing

The past decade has witnessed a rapid evolution in identifying more versatile clustered regularly interspaced short palindromic repeats(CRISPR)/CRISPR-associated protein(Cas)nucleases and their functional variants,as well as in developing precise CRISPR/Cas-derived genome editors.The programmable and robust features of the genome editors provide an effective RNAguided platform for fundamental life science research and subsequent applications in diverse scenarios,including biomedical innovation and targeted crop improvement.One of the most essential principles is to guide alterations in genomic sequences or genes in the intended manner without undesired off-target impacts,which strongly depends on the efficiency and specificity of single guide RNA(sgRNA)-directed recognition of targeted DNA sequences.Recent advances in empirical scoring algorithms and machine learning models have facilitated sgRNA design and off-target prediction.In this review,we first briefly introduce the different features of CRISPR/Cas tools that should be taken into consideration to achieve specific purposes.Secondly,we focus on the computer-assisted tools and resources that are widely used in designing sgRNAs and analyzing CRISPR/Cas-induced on-and off-target mutations.Thirdly,we provide insights into the limitations of available computational tools that would help researchers of this field for further optimization.Lastly,we suggest a simple but effective workflow for choosing and applying web-based resources and tools for CRISPR/Cas genome editing.

Computational Methods for Single-cell DNA Methylome Analysis

Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity.Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution.While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships,they pose new challenges in data processing and interpretation.This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis.We discuss critical components of single-cell DNA methylome data analysis,including data preprocessing,quality control,imputation,dimensionality reduction,cell clustering,supervised cell annotation,cell lineage reconstruction,gene activity scoring,and integration with transcriptome data.We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes.Finally,we discuss existing challenges and opportunities for future development.

Computational Approaches for Prioritizing Candidate Disease Genes Based on PPI Networks

With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations,many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction（PPI） networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.

Geometry-based graphical methods for solar control in architecture:A digital framework

The form of a building is among the most critical design aspects concerning building energy consumption.Form-based passive design strategies,like solar control,can significantly reduce heating and cooling demands if implemented early in the design process.In this sense,there is an evident need for tools that can adequately support designers in their decisions.This paper aims to illustrate how geometry-based graphical methods(GGM)can provide effective support in the conceptual design stage.The paper introduces a novel digital framework for designing and analysing shading devices that leverages geometrical models and graphical methods.The digital implementation of GGM allows extending their applicability to threedimensional and non-planar geometries.A comprehensive review of existing methods and tools for the design of shading devices lays the ground for the proposed digital framework,which is then demonstrated through two case studies.The results show that the diagrammatic nature of GGM facilitates a better and more direct understanding of the relationship between form and performance.

Making Canonical Workflow Building Blocks Interoperable across Workflow Languages

We introduce the concept of Canonical Workflow Building Blocks(CWBB),a methodology of describing and wrapping computational tools,in order for them to be utilised in a reproducible manner from multiple workflow languages and execution platforms.The concept is implemented and demonstrated with the BioExcel Building Blocks library(BioBB),a collection of tool wrappers in the field of computational biomolecular simulation.Interoperability across different workflow languages is showcased through a protein Molecular Dynamics setup transversal workflow,built using this library and run with 5 different Workflow Manager Systems(WfMS).We argue such practice is a necessary requirement for FAIR Computational Workflows and an element of Canonical Workflow Frameworks for Research(CWFR)in order to improve widespread adoption and reuse of computational methods across workflow language barriers.