Application of biological big data and radiomics in hepatocellular carcinoma

Hepatocellular carcinoma(HCC),one of the most common gastrointestinal cancers,has been considered a worldwide threat due to its high incidence and poor prognosis.In recent years,with the continuous emergence and promotion of new sequencing technologies in omics,genomics,transcriptomics,proteomics,and liquid biopsy are used to assess HCC heterogeneity from different perspectives and become a hotspot in the field of tumor precision medicine.In addition,with the continuous improvement of machine learning algorithms and deep learning algorithms,radiomics has made great progress in the field of ultrasound,CT and MRI for HCC.This article mainly reviews the research progress of biological big data and radiomics in HCC,and it provides new methods and ideas for the diagnosis,prognosis,and therapy of HCC.

Engineering the future cereal crops with big biological data:toward intelligence-driven breeding by design

How to feed 10 billion human populations is one of the challenges that need to be addressed in the following decades,especially under an unpredicted climate change.Crop breeding,initiating from the phenotype-based selection by local farmers and developing into current biotechnology-based breeding,has played a critical role in securing the global food supply.However,regarding the changing environment and ever-increasing human population,can we breed outstanding crop varieties fast enough to achieve high productivity,good quality,and widespread adaptability?This review outlines the recent achievements in understanding cereal crop breeding,including the current knowledge about crop agronomic traits,newly developed techniques,crop big biological data research,and the possibility of integrating them for intelligence-driven breeding by design,which ushers in a new era of crop breeding practice and shapes the novel architecture of future crops.This review focuses on the major cereal crops,including rice,maize,and wheat,to explain how intelligence-driven breeding by design is becoming a reality.

TBtools-II: A “one for all, all for one” bioinformatics platform for biological big-data mining

Since the official release of the stand-alone bioinformatics toolkit TBtools in 2020,its superior functionality in data analysis has been demonstrated by its widespread adoption by many thousands of users and references in more than 5000 academic articles.Now,TBtools is a commonly used tool in biological laboratories.Over the past 3 years,thanks to invaluable feedback and suggestions from numerous users,we have optimized and expanded the functionality of the toolkit,leading to the development of an upgraded version—TBtools-II.In this upgrade,we have incorporated over 100 new features,such as those for comparative genomics analysis,phylogenetic analysis,and data visualization.Meanwhile,to better meet the increasing needs of personalized data analysis,we have launched the plugin mode,which enables users to develop their own plugins and manage their selection,installation,and removal according to individual needs.To date,the plugin store has amassed over 50 plugins,with more than half of them being independently developed and contributed by TBtools users.These plugins offer a range of data analysis options including co-expression network analysis,single-cell data analysis,and bulked segregant analysis sequencing data analysis.Overall,TBtools is now transforming from a stand-alone software to a comprehensive bioinformatics platform of a vibrant and cooperative community in which users are also developers and contributors.By promoting the theme“one for all,all for one”,we believe that TBtools-II will greatly benefit more biological researchers in this big-data era.

Big Biological Data:Challenges and Opportunities

In ＂Omics＂ era of the life sciences, data is presented in many forms, which represent the information at various levels of bio- logical systems, including data about genome, transcriptome, epigenome, proteome, metabolome, molecular imaging, molec- ular pathways, different population of people and clinical/med- ical records. The biological data is big, and its scale has already been well beyond petabyte （PB） even exabyte （EB）. Nobody doubts that the biological data will create huge amount of val- ues, if scientists can overcome many challenges, e.g., how to handle the complexity of information, how to integrate the data from very heterogeneous resources, what kind of principles or standards to be adopted when facing with the big data. Tools and techniques for analyzing big biological data enable us to translate massive amount of information into a better under- standing of the basic biomedical mechanisms, which can be fur- ther applied to translational or personalized medicine.

Diagnosing phenotypes of single-sample individuals by edge biomarkers

Network or edge biomarkers area reliable form to characterize phenotypes or diseases.However,obtaining edges orcorrelations between molecules for an individual requires measurement ofmultiple samples of that individual,which are generally unavailable in clinical practice.Thus,it is strongly demanded to diagnose a disease by edge or network biomarkers in one-sample-for-one-individual context.Here,we developed a new computational framework,EdgeBiomarker,to integrate edge and node biomarkers to diagnose phenotype of each single test sample.By applying the method to datasets of lung and breast cancer,it reveals new marker genes/gene-pairs and related sub-networks for distinguishing earlier and advanced cancer stages.Our method shows advantages over traditional methods:(i)edge biomarkers extracted from non-differentially expressed genes achieve better cross-validation accuracy of diagnosis than molecule or node biomarkers from differentially expressed genes,suggesting that certain pathogenic information is only present at the level of network and under-estimated by traditional methods;(ii)edge biomarkers categorize patients into low/high survival rate in a more reliablemanner;(iii)edge biomarkers are significantly enriched in relevant biological functions or pathways,implying that the association changes ina network,rather than expression changes in individual molecules,tendtobe causally related to cancer development.The new frameworkof edgebiomarkers paves theway for diagnosing diseases and analyzing the irmolecular mechanisms by edges or networks in one-sample-for-one-individual basis.This also provides a powerful tool for precision medicine or big-data medicine.