ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes

Recent advances in the characterization of essential genes and development of a database of essential genes

Over the past few decades, there has been a significant interest in the study of essential genes, which are crucial for the survival of an organism under specific environmental conditions and thus have practical applications in the fields of synthetic biology and medicine. An increasing amount of experimental data on essential genes has been obtained with the continuous development of technological methods. Meanwhile, various computational prediction methods, related databases and web servers have emerged accordingly. To facilitate the study of essential genes, we have established a database of essential genes (DEG), which has become popular with continuous updates to facilitate essential gene feature analysis and prediction, drug and vaccine development, as well as artificial genome design and construction. In this article, we summarized the studies of essential genes, overviewed the relevant databases, and discussed their practical applications. Furthermore, we provided an overview of the main applications of DEG and conducted comprehensive analyses based on its latest version. However, it should be noted that the essential gene is a dynamic concept instead of a binary one, which presents both opportunities and challenges for their future development.

The whole genomic analysis of the Orf virus strains ORFV-SC and ORFV-SC1 from the Sichuan province and their weak pathological response in rabbits

The Orf virus (ORFV) is a member of the Parapoxvirus genus of the Poxviridae family and can cause contagious diseases in sheep, goats, and wild ungulates. In the present study, two ORFV isolates (ORFV-SC isolated from Sichuan province and ORFV-SC1 produced by 60 passages of ORFV-SC in cells) were sequenced and compared to multiple ORFVs. The two ORFV sequences had entire genome sizes of 14,0707 bp and 141,154 bp, respectively, containing 130 and 131 genes, with a G + C content of 63% for the ORFV-SC sequence and 63.9% for the ORFV-SC1 sequence. Alignment of ORFV-SC and ORFV-SC1 with five other ORFV isolates revealed that ORFV-SC, ORFV-SC1, and NA1/11 shared > 95% nucleotide identity with 109 genes. Five genes (ORF007, ORF20, ORF080, ORF112, ORF116) have low amino acids identity between ORFV-SC and ORFV-SC1. Mutations in amino acids result in changes in the secondary and tertiary structure of ORF007, ORF020, and ORF112 proteins. The phylogenetic tree based on the complete genome sequence and 37 single genes revealed that the two ORFV isolates originated from sheep. Finally, animal experiments demonstrated that ORFV-SC1 is less harmful to rabbits than ORFV-SC. The exploration of two full-length viral genome sequences provides valuable information in ORFV biology and epidemiology research. Furthermore, ORFV-SC1 demonstrated an acceptable safety profile following animal vaccination, indicating its potential as a live ORFV vaccine.

Prediction of protein-coding small ORFs in multi-species using integrated sequence-derived features and the random forest model

Proteins encoded by small open reading frames (sORFs) can serve as functional elements playing important roles in vivo. Such sORFs also constitute the potential pool for facilitating the de novo gene birth, driving evolutionary innovation and species diversity. Therefore, their theoretical and experimental identification has become a critical issue. Herein, we proposed a protein-coding sORFs prediction method merely based on integrative sequence-derived features. Our prediction performance is better or comparable compared with other nine prevalent methods, which shows that our method can provide a relatively reliable research tool for the prediction of protein-coding sORFs. Our method allows users to estimate the potential expression of a queried sORF, which has been demonstrated by the correlation analysis between our possibility estimation and codon adaption index (CAI). Based on the features that we used, we demonstrated that the sequence features of the protein-coding sORFs in the two domains have significant differences implying that it might be a relatively hard task in terms of cross-domain prediction, hence domain-specific models were developed, which allowed users to predict protein-coding sORFs both in eukaryotes and prokaryotes. Finally, a web-server was developed and provided to boost and facilitate the study of the related field, which is freely available at http://guolab.whu.edu.cn/codingCapacity/index.html.

Identify essential genes based on clustering based synthetic minority oversampling technique

Prediction of essential genes in a life organism is one of the central tasks in synthetic biology. Computational predictors are desired because experimental data is often unavailable. Recently, some sequence-based predictors have been constructed to identify essential genes. However, their predictive performance should be further improved. One key problem is how to effectively extract the sequence-based features, which are able to discriminate the essential genes. Another problem is the imbalanced training set. The amount of essential genes in human cell lines is lower than that of non-essential genes. Therefore, predictors trained with such imbalanced training set tend to identify an unseen sequence as a non-essential gene. Here, a new over-sampling strategy was proposed called Clustering based Synthetic Minority Oversampling Technique (CSMOTE) to overcome the imbalanced data issue. Combining CSMOTE with the Z curve, the global features, and Support Vector Machines, a new protocol called iEsGene-CSMOTE was proposed to identify essential genes. The rigorous jackknife cross validation results indicated that iEsGene-CSMOTE is better than the other competing methods. The proposed method outperformed λ-interval Z curve by 35.48% and 11.25% in terms of Sn and BACC, respectively.

Recombineering in Non-Model Bacteria

The technology of recombineering, in vivo genetic engineering, was initially developed in Escherichia coli and uses bacteriophage-encoded homologous recombination proteins to efficiently recombine DNA at short homologies (35 to 50 nt). Because the technology is homology driven, genomic DNA can be modified precisely and independently of restriction site location. Recombineering uses linear DNA substrates that are introduced into the cell by electroporation; these can be PCR products, synthetic double-strand DNA (dsDNA), or single-strand DNA (ssDNA). Here we describe the applications, challenges, and factors affecting ssDNA and dsDNA recombineering in a variety of non-model bacteria, both Gram-negative and -positive, and recent breakthroughs in the field. We list different microbes in which the widely used phage λ Red and Rac RecET recombination systems have been used for in vivo genetic engineering. New homologous ssDNA and dsDNA recombineering systems isolated from non-model bacteria are also described. The Basic Protocol outlines a method for ssDNA recombineering in the non-model species of Shewanella. The Alternate Protocol describes the use of CRISPR/Cas as a counter-selection system in conjunction with recombineering to enhance recovery of recombinants. We provide additional background information, pertinent considerations for experimental design, and parameters critical for success. The design of ssDNA oligonucleotides (oligos) and various internet-based tools for oligo selection from genome sequences are also described, as is the use of oligo-mediated recombination. This simple form of genome editing uses only ssDNA oligo(s) and does not require an exogenous recombination system. The information presented here should help researchers identify a recombineering system suitable for their microbe(s) of interest. If no system has been characterized for a specific microbe, researchers can find guidance in developing a recombineering system from scratch. We provide a flowchart of decision-making paths for strategically applying annealase-dependent or oligo-mediated recombination in non-model and undomesticated bacteria. © 2022 Wiley Periodicals LLC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. Basic Protocol: ssDNA recombineering in Shewanella species Alternate Protocol: ssDNA recombineering coupled to CRISPR/Cas9 in Shewanella species.

Bacterial genome reductions: Tools, applications, and challenges

Bacterial cells are widely used to produce value-added products due to their versatility, ease of manipulation, and the abundance of genome engineering tools. However, the efficiency of producing these desired biomolecules is often hindered by the cells’ own metabolism, genetic instability, and the toxicity of the product. To overcome these challenges, genome reductions have been performed, making strains with the potential of serving as chassis for downstream applications. Here we review the current technologies that enable the design and construction of such reduced-genome bacteria as well as the challenges that limit their assembly and applicability. While genomic reductions have shown improvement of many cellular characteristics, a major challenge still exists in constructing these cells efficiently and rapidly. Computational tools have been created in attempts at minimizing the time needed to design these organisms, but gaps still exist in modelling these reductions in silico. Genomic reductions are a promising avenue for improving the production of value-added products, constructing chassis cells, and for uncovering cellular function but are currently limited by their time-consuming construction methods. With improvements to and the creation of novel genome editing tools and in silico models, these approaches could be combined to expedite this process and create more streamlined and efficient cell factories.

CEPZ: A Novel Predictor for Identification of DNase I Hypersensitive Sites

DNase I hypersensitive sites (DHSs) have proven to be tightly associated with cis-regulatory elements, commonly indicating specific function on the chromatin structure. Thus, identifying DHSs plays a fundamental role in decoding gene regulatory behavior. While traditional experimental methods turn to be time-consuming and expensive, computational techniques promise to be practical to discovering and analyzing regulatory factors. In this study, we applied an efficient model that considered composition information and physicochemical properties and effectively selected features with a boosting algorithm. CEPZ, our predictor, greatly improved a Matthews correlation coefficient and accuracy of 0.7740 and 0.9113 respectively, more competitive than any predictor before. This result suggests that it may become a useful tool for DHSs research in the human and other complex genomes. Our research was anchored on the properties of dinucleotides and we identified several dinucleotides with significant differences in the distribution of DHS and non-DHS samples, which are likely to have a special meaning in the chromatin structure. The datasets, feature sets and the relevant algorithm are available at https://github.com/YanZheng-16/CEPZ_DHS/.

CasLocusAnno: a web-based server for annotating cas loci and their corresponding (sub)types

In prokaryotes, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated protein (Cas) systems constitute adaptive immune systems against mobile genetic elements (MGEs). Here, we introduce the Markov cluster algorithm (MCL) to Makarova et al.'s method in order to select a more reasonable profile. Additionally, our new Maximum Continuous Cas Subcluster (MCCS) method helps identification of tightly clustered loci. The comparison with two other commonly used programs shows that the method could identify Cas proteins with higher accuracy and lower Additional Prediction Rate (APR). Moreover, we developed a web-based server, CasLocusAnno (http://cefg.uestc.cn/CasLocusAnno), capable of annotating Cas proteins, cas loci and their (sub)types less than ~ 28 s following the whole proteome sequence submission. Its standalone version can be downloaded at https://github.com/RiversDong/CasLocusAnno.

Identifying Bacterial Essential Genes Based on a Feature-Integrated Method

Essential genes are those genes of an organism that are considered to be crucial for its survival. Identification of essential genes is therefore of great significance to advance our understanding of the principles of cellular life. We have developed a novel computational method, which can effectively predict bacterial essential genes by extracting and integrating homologous features, protein domain feature, gene intrinsic features, and network topological features. By performing the principal component regression (PCR) analysis for Escherichia coli MG1655, we established a classification model with the average area under curve (AUC) value of 0.992 in ten times 5-fold cross-validation tests. Furthermore, when employing this new model to a distantly related organism-Streptococcus pneumoniae TIGR4, we still got a reliable AUC value of 0.788. These results indicate that our feature-integrated approach could have practical applications in accurately investigating essential genes from broad bacterial species, and also provide helpful guidelines for the minimal cell.

iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC

Meiotic recombination caused by meiotic double-strand DNA breaks. In some regions the frequency of DNA recombination is relatively higher, while in other regions the frequency is lower: the former is usually called "recombination hotspot", while the latter the "recombination coldspot". Information of the hot and cold spots may provide important clues for understanding the mechanism of genome revolution. Therefore, it is important to accurately predict these spots. In this study, we rebuilt the benchmark dataset by unifying its samples with a same length (131 bp). Based on such a foundation and using SVM (Support Vector Machine) classifier, a new predictor called "iRSpot-Pse6NC" was developed by incorporating the key hexamer features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. It has been observed via rigorous cross-validations that the proposed predictor is superior to its counterparts in overall accuracy, stability, sensitivity and specificity. For the convenience of most experimental scientists, the web-server for iRSpot-Pse6NC has been established at http://lin-group.cn/server/iRSpot-Pse6NC, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.

HBPred: a tool to identify growth hormone-binding proteins

Hormone-binding protein (HBP) is a kind of soluble carrier protein and can selectively and non-covalently interact with hormone. HBP plays an important role in life growth, but its function is still unclear. Correct recognition of HBPs is the first step to further study their function and understand their biological process. However, it is difficult to correctly recognize HBPs from more and more proteins through traditional biochemical experiments because of high experimental cost and long experimental period. To overcome these disadvantages, we designed a computational method for identifying HBPs accurately in the study. At first, we collected HBP data from UniProt to establish a high-quality benchmark dataset. Based on the dataset, the dipeptide composition was extracted from HBP residue sequences. In order to find out the optimal features to provide key clues for HBP identification, the analysis of various (ANOVA) was performed for feature ranking. The optimal features were selected through the incremental feature selection strategy. Subsequently, the features were inputted into support vector machine (SVM) for prediction model construction. Jackknife cross-validation results showed that 88.6% HBPs and 81.3% non-HBPs were correctly recognized, suggesting that our proposed model was powerful. This study provides a new strategy to identify HBPs. Moreover, based on the proposed model, we established a webserver called HBPred, which could be freely accessed at http://lin-group.cn/server/HBPred.

PRWHMDA: Human Microbe-Disease Association Prediction by Random Walk on the Heterogeneous Network with PSO

Microorganisms resided in human body play a vital role in metabolism, immune defense, nutrition absorption, cancer control and protection against pathogen colonization. The changes of microbial communities can cause human diseases. Based on the known microbe-disease association, we presented a novel computational model employing Random Walking with Restart optimized by Particle Swarm Optimization (PSO) on the heterogeneous interlinked network of Human Microbe-Disease Associations (PRWHMDA) (see Figure 1). Based on the known human microbe-disease associations, we constructed the heterogeneous interlinked network with Cosine similarity. The extended random walk with restart (RWR) method was derived to get the potential microbe-disease associations. PSO was utilized to get the optimal parameters of RWR. To evaluate the prediction effectiveness, we performed leave one out cross validation (LOOCV) and 5-fold cross validation (CV), which got the AUC (The area under ROC curve) of 0.915 (LOOCV) and the average AUCs of 0.8875 ± 0.0046 (5-fold CV). Moreover, we carried out three case studies of asthma, inflammatory bowel disease (IBD) and type 1 diabetes (T1D) for the further evaluation. The result showed that 10, 10 and 9 of top-10 predicted microbes were verified by previously published experimental results, respectively. It is anticipated that PRWHMDA can be effective to identify the disease-related microbes and maybe helpful to disclose the relationship between microorganisms and their human host.

Accurate prediction of human essential genes using only nucleotide composition and association information

Motivation

Previously constructed classifiers in predicting eukaryotic essential genes integrated a variety of features including experimental ones. If we can obtain satisfactory prediction using only nucleotide (sequence) information, it would be more promising. Three groups recently identified essential genes in human cancer cell lines using wet experiments and it provided wonderful opportunity to accomplish our idea. Here we improved the Z curve method into the λ-interval form to denote nucleotide composition and association information and used it to construct the SVM classifying model.

Results

Our model accurately predicted human gene essentiality with an AUC higher than 0.88 both for 5-fold cross-validation and jackknife tests. These results demonstrated that the essentiality of human genes could be reliably reflected by only sequence information. We re-predicted the negative dataset by our Pheg server and 118 genes were additionally predicted as essential. Among them, 20 were found to be homologues in mouse essential genes, indicating that some of the 118 genes were indeed essential, however previous experiments overlooked them. As the first available server, Pheg could predict essentiality for anonymous gene sequences of human. It is also hoped the λ-interval Z curve method could be effectively extended to classification issues of other DNA elements.

Availability and Implementation

http://cefg.uestc.edu.cn/Pheg.

Contact

fbguo@uestc.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Zisland Explorer: detect genomic islands by combining homogeneity and heterogeneity properties

Genomic islands are genomic fragments of alien origin in bacterial and archaeal genomes, usually involved in symbiosis or pathogenesis. In this work, we described Zisland Explorer, a novel tool to predict genomic islands based on the segmental cumulative GC profile. Zisland Explorer was designed with a novel strategy, as well as a combination of the homogeneity and heterogeneity of genomic sequences. While the sequence homogeneity reflects the composition consistence within each island, the heterogeneity measures the composition bias between an island and the core genome. The performance of Zisland Explorer was evaluated on the data sets of 11 different organisms. Our results suggested that the true-positive rate (TPR) of Zisland Explorer was at least 10.3% higher than that of four other widely used tools. On the other hand, the new tool did not lose overall accuracy with the improvement in the TPR and showed better equilibrium among various evaluation indexes. Also, Zisland Explorer showed better accuracy in the prediction of experimental island data. Overall, the tool provides an alternative solution over other tools, which expands the field of island prediction and offers a supplement to increase the performance of the distinct predicting strategy. We have provided a web service as well as a graphical user interface and open-source code across multiple platforms for Zisland Explorer, which is available at http://cefg.uestc.edu.cn/Zisland_Explorer/ or http://tubic.tju.edu.cn/Zisland_Explorer/.

A Comprehensive Overview of Online Resources to Identify and Predict Bacterial Essential Genes

Genes critical for the survival or reproduction of an organism in certain circumstances are classified as essential genes. Essential genes play a significant role in deciphering the survival mechanism of life. They may be greatly applied to pharmaceutics and synthetic biology. The continuous progress of experimental method for essential gene identification has accelerated the accumulation of gene essentiality data which facilitates the study of essential genes in silico. In this article, we present some available online resources related to gene essentiality, including bioinformatic software tools for transposon sequencing (Tn-seq) analysis, essential gene databases and online services to predict bacterial essential genes. We review several computational approaches that have been used to predict essential genes, and summarize the features used for gene essentiality prediction. In addition, we evaluate the available online bacterial essential gene prediction servers based on the experimentally validated essential gene sets of 30 bacteria from DEG. This article is intended to be a quick reference guide for the microbiologists interested in the essential genes.

Complete genome sequences of two novel autographiviruses infecting a bacterium from the Pseudomonas fluorescens group

In this paper, we describe two independent isolates of a new member of the subfamily Autographivirinae, Pseudomonas phage KNP. The type strain (KNP) has a linear, 40,491-bp-long genome with GC content of 57.3%, and 50 coding DNA sequences (CDSs). The genome of the second strain (WRT) contains one CDS less, encodes a significantly different tail fiber protein and is shorter (40,214 bp; GC content, 57.4%). Phylogenetic analysis indicates that both KNP and WRT belong to the genus T7virus. Together with genetically similar Pseudomonas phages (gh-1, phiPSA2, phiPsa17, PPPL-1, shl2, phi15, PPpW-4, UNO-SLW4, phiIBB-PF7A, Pf-10, and Phi-S1), they form a divergent yet coherent group that stands apart from the T7-like viruses (sensu lato). Analysis of the diversity of this group and its relatedness to other members of the subfamily Autographivirinae led us to the conclusion that this group might be considered as a candidate for a new genus.

Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots

Pseudo dinucleotide composition (PseDNC) and Z curve showed excellent performance in the classification issues of nucleotide sequences in bioinformatics. Inspired by the principle of Z curve theory, we improved PseDNC to give the phase-specific PseDNC (psPseDNC). In this study, we used the prediction of recombination spots as a case to illustrate the capability of psPseDNC and also PseDNC fused with Z curve theory based on a novel machine learning method named large margin distribution machine (LDM). We verified that combining the two widely used approaches could generate better performance compared to only using PseDNC with a support vector machine based (SVM-based) model. The best Mathew's correlation coefficient (MCC) achieved by our LDM-based model was 0.7037 through the rigorous jackknife test and improved by ∼6.6%, ∼3.2%, and ∼2.4% compared with three previous studies. Similarly, the accuracy was improved by 3.2% compared with our previous iRSpot-PseDNC web server through an independent data test. These results demonstrate that the joint use of PseDNC and Z curve enhances performance and can extract more information from a biological sequence. To facilitate research in this area, we constructed a user-friendly web server for predicting hot/cold spots, HcsPredictor, which can be freely accessed from . In summary, we provided a united algorithm by integrating Z curve with PseDNC. We hope this united algorithm could be extended to other classification issues in DNA elements.

RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation

Background

RNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms.

Results

An alignment-free RNA comparison algorithm was proposed, in which novel numerical representations RNA-TVcurve (triple vector curve representation) of RNA sequence and corresponding secondary structure features are provided. Then a multi-scale similarity score of two given RNAs was designed based on wavelet decomposition of their numerical representation. In support of RNA mutation and phylogenetic analysis, a web server (RNA-TVcurve) was designed based on this alignment-free RNA comparison algorithm. It provides three functional modules: 1) visualization of numerical representation of RNA secondary structure; 2) detection of single-point mutation based on secondary structure; and 3) comparison of pairwise and multiple RNA secondary structures. The inputs of the web server require RNA primary sequences, while corresponding secondary structures are optional. For the primary sequences alone, the web server can compute the secondary structures using free energy minimization algorithm in terms of RNAfold tool from Vienna RNA package.

Conclusion

RNA-TVcurve is the first integrated web server, based on an alignment-free method, to deliver a suite of RNA analysis functions, including visualization, mutation analysis and multiple RNAs structure comparison. The comparison results with two popular RNA comparison tools, RNApdist and RNAdistance, showcased that RNA-TVcurve can efficiently capture subtle relationships among RNAs for mutation detection and non-coding RNA classification. All the relevant results were shown in an intuitive graphical manner, and can be freely downloaded from this server. RNA-TVcurve, along with test examples and detailed documents, are available at: http://ml.jlu.edu.cn/tvcurve/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1481-7) contains supplementary material, which is available to authorized users.

Global insights into acetic acid resistance mechanisms and genetic stability of Acetobacter pasteurianus strains by comparative genomics

Acetobacter pasteurianus (Ap) CICC 20001 and CGMCC 1.41 are two acetic acid bacteria strains that, because of their strong abilities to produce and tolerate high concentrations of acetic acid, have been widely used to brew vinegar in China. To globally understand the fermentation characteristics, acid-tolerant mechanisms and genetic stabilities, their genomes were sequenced. Genomic comparisons with 9 other sequenced Ap strains revealed that their chromosomes were evolutionarily conserved, whereas the plasmids were unique compared with other Ap strains. Analysis of the acid-tolerant metabolic pathway at the genomic level indicated that the metabolism of some amino acids and the known mechanisms of acetic acid tolerance, might collaboratively contribute to acetic acid resistance in Ap strains. The balance of instability factors and stability factors in the genomes of Ap CICC 20001 and CGMCC 1.41 strains might be the basis for their genetic stability, consistent with their stable industrial performances. These observations provide important insights into the acid resistance mechanism and the genetic stability of Ap strains and lay a foundation for future genetic manipulation and engineering of these two strains.

Genome Sequence of Bacillus endophyticus and Analysis of Its Companion Mechanism in the Ketogulonigenium vulgare-Bacillus Strain Consortium

Bacillus strains have been widely used as the companion strain of Ketogulonigenium vulgare in the process of vitamin C fermentation. Different Bacillus strains generate different effects on the growth of K. vulgare and ultimately influence the productivity. First, we identified that Bacillus endophyticus Hbe603 was an appropriate strain to cooperate with K. vulgare and the product conversion rate exceeded 90% in industrial vitamin C fermentation. Here, we report the genome sequencing of the B. endophyticus Hbe603 industrial companion strain and speculate its possible advantage in the consortium. The circular chromosome of B. endophyticus Hbe603 has a size of 4.87 Mb with GC content of 36.64% and has the highest similarity with that of Bacillus megaterium among all the bacteria with complete genomes. By comparing the distribution of COGs with that of Bacillus thuringiensis, Bacillus cereus and B. megaterium, B. endophyticus has less genes related to cell envelope biogenesis and signal transduction mechanisms, and more genes related to carbohydrate transport and metabolism, energy production and conversion, as well as lipid transport and metabolism. Genome-based functional studies revealed the specific capability of B. endophyticus in sporulation, transcription regulation, environmental resistance, membrane transportation, extracellular proteins and nutrients synthesis, which would be beneficial for K. vulgare. In particular, B. endophyticus lacks the Rap-Phr signal cascade system and, in part, spore coat related proteins. In addition, it has specific pathways for vitamin B12 synthesis and sorbitol metabolism. The genome analysis of the industrial B. endophyticus will help us understand its cooperative mechanism in the K. vulgare-Bacillus strain consortium to improve the fermentation of vitamin C.