A multi-sample approach increases the accuracy of transcript assembly

High-quality chromosome scale genome assemblies of two important Sorghum inbred lines, Tx2783 and RTx436

Sorghum bicolor (L.) Moench is a significant grass crop globally, known for its genetic diversity. High quality genome sequences are needed to capture the diversity. We constructed high-quality, chromosome-level genome assemblies for two vital sorghum inbred lines, Tx2783 and RTx436. Through advanced single-molecule techniques, long-read sequencing and optical maps, we improved average sequence continuity 19-fold and 11-fold higher compared to existing Btx623 v3.0 reference genome and obtained 19 and 18 scaffolds (N50 of 25.6 and 14.4) for Tx2783 and RTx436, respectively. Our gene annotation efforts resulted in 29 612 protein-coding genes for the Tx2783 genome and 29 265 protein-coding genes for the RTx436 genome. Comparative analyses with 26 plant genomes which included 18 sorghum genomes and 8 outgroup species identified around 31 210 protein-coding gene families, with about 13 956 specific to sorghum. Using representative models from gene trees across the 18 sorghum genomes, a total of 72 579 pan-genes were identified, with 14% core, 60% softcore and 26% shell genes. We identified 99 genes in Tx2783 and 107 genes in RTx436 that showed functional enrichment specifically in binding and metabolic processes, as revealed by the GO enrichment Pearson Chi-Square test. We detected 36 potential large inversions in the comparison between the BTx623 Bionano map and the BTx623 v3.1 reference sequence. Strikingly, these inversions were notably absent when comparing Tx2783 or RTx436 with the BTx623 Bionano map. These inversion were mostly in the pericentromeric region which is known to have low complexity regions and harder to assemble and suggests the presence of potential artifacts in the public BTx623 reference assembly. Furthermore, in comparison to Tx2783, RTx436 exhibited 324 883 additional Single Nucleotide Polymorphisms (SNPs) and 16 506 more Insertions/Deletions (INDELs) when using BTx623 as the reference genome. We also characterized approximately 348 nucleotide-binding leucine-rich repeat (NLR) disease resistance genes in the two genomes. These high-quality genomes serve as valuable resources for discovering agronomic traits and structural variation studies.

The reference genome of an endangered Asteraceae, Deinandra increscens subsp. villosa, endemic to the Central Coast of California

We present a reference genome for the federally endangered Gaviota tarplant, Deinandra increscens subsp. villosa (Madiinae, Asteraceae), an annual herb endemic to the Central California coast. Generating PacBio HiFi, Oxford Nanopore Technologies, and Dovetail Omni-C data, we assembled a haploid consensus genome of 1.67 Gb as 28.7 K scaffolds with a scaffold N50 of 74.9 Mb. We annotated repeat content in 74.8% of the genome. Long terminal repeats (LTRs) covered 44.0% of the genome with Copia families predominant at 22.9% followed by Gypsy at 14.2%. Both Gypsy and Copia elements were common in ancestral peaks of LTRs, and the most abundant element was a Gypsy element containing nested Copia/Angela sequence similarity, reflecting a complex evolutionary history of repeat activity. Gene annotation produced 33,257 genes and 68,942 transcripts, of which 99% were functionally annotated. BUSCO scores for the annotated proteins were 96.0% complete of which 77.6% was single copy and 18.4% duplicates. Whole genome duplication synonymous mutation rates of Gaviota tarplant and sunflower (Helianthus annuus) shared peaks that correspond to the last Asteraceae polyploidization event and subsequent divergence from a common ancestor at ∼27 MYA. Regions of high-density tandem genes were identified, pointing to potentially important loci of environmental adaptation in this species.

Accurate assembly of multiple RNA-seq samples with Aletsch

MOTIVATION

High-throughput RNA sequencing has become indispensable for decoding gene activities, yet the challenge of reconstructing full-length transcripts persists. Traditional single-sample assemblers frequently produce fragmented transcripts, especially in single-cell RNA-seq data. While algorithms designed for assembling multiple samples exist, they encounter various limitations.

RESULTS

We present Aletsch, a new assembler for multiple bulk or single-cell RNA-seq samples. Aletsch incorporates several algorithmic innovations, including a "bridging" system that can effectively integrate multiple samples to restore missed junctions in individual samples, and a new graph-decomposition algorithm that leverages "supporting" information across multiple samples to guide the decomposition of complex vertices. A standout feature of Aletsch is its application of a random forest model with 50 well-designed features for scoring transcripts. We demonstrate its robust adaptability across different chromosomes, datasets, and species. Our experiments, conducted on RNA-seq data from several protocols, firmly demonstrate Aletsch's significant outperformance over existing meta-assemblers. As an example, when measured with the partial area under the precision-recall curve (pAUC, constrained by precision), Aletsch surpasses the leading assemblers TransMeta by 22.9%-62.1% and PsiCLASS by 23.0%-175.5% on human datasets.

AVAILABILITY AND IMPLEMENTATION

Aletsch is freely available at https://github.com/Shao-Group/aletsch. Scripts that reproduce the experimental results of this manuscript is available at https://github.com/Shao-Group/aletsch-test.

GeneMark-ETP significantly improves the accuracy of automatic annotation of large eukaryotic genomes

Large-scale genomic initiatives, such as the Earth BioGenome Project, require efficient methods for eukaryotic genome annotation. Here we present an automatic gene finder, GeneMark-ETP, integrating genomic-, transcriptomic-, and protein-derived evidence that has been developed with a focus on large plant and animal genomes. GeneMark-ETP first identifies genomic loci where extrinsic data are sufficient for making gene predictions with "high confidence." The genes situated in the genomic space between the high-confidence genes are predicted in the next stage. The set of high-confidence genes serves as an initial training set for the statistical model. Further on, the model parameters are iteratively updated in the rounds of gene prediction and parameter re-estimation. Upon reaching convergence, GeneMark-ETP makes the final predictions and delivers the whole complement of predicted genes. GeneMark-ETP outperforms gene finders using a single type of extrinsic evidence. Comparisons with gene finders MAKER2 and TSEBRA, those that use both transcript- and protein-derived extrinsic evidence, show that GeneMark-ETP delivers state-of-the-art gene-prediction accuracy, with the margin of outperforming existing approaches increasing in its application to larger and more complex eukaryotic genomes.

A new gene finding tool GeneMark-ETP significantly improves the accuracy of automatic annotation of large eukaryotic genomes

Large-scale genomic initiatives, such as the Earth BioGenome Project, require efficient methods for eukaryotic genome annotation. Here we present an automatic gene finder, GeneMark-ETP, integrating genomic-, transcriptomic- and protein-derived evidence that has been developed with a focus on large plant and animal genomes. GeneMark-ETP first identifies genomic loci where extrinsic data is sufficient for making gene predictions with 'high confidence'. The genes situated in the genomic space between the high confidence genes are predicted in the next stage. The set of high confidence genes serves as an initial training set for the statistical model. Further on, the model parameters are iteratively updated in the rounds of gene prediction and parameter re-estimation. Upon reaching convergence, GeneMark-ETP makes the final predictions and delivers the whole complement of predicted genes. GeneMark-ETP outperformed gene finders using a single type of extrinsic evidence. Comparisons with gene finders utilizing both transcript- and protein-derived extrinsic evidence, MAKER2, and TSEBRA, demonstrated that GeneMark-ETP delivered state-of-the-art gene prediction accuracy with the margin of outperforming existing approaches increasing in its applications to larger and more complex eukaryotic genomes.

A chromosome-level genome assembly of a free-living white-crowned sparrow (Zonotrichia leucophrys gambelii)

The white-crowned sparrow, Zonotrichia leucophrys, is a passerine bird with a wide distribution and it is extensively adapted to environmental changes. It has historically acted as a model species in studies on avian ecology, physiology and behaviour. Here, we present a high-quality chromosome-level genome of Zonotrichia leucophrys using PacBio and OmniC sequencing data. Gene models were constructed by combining RNA-seq and Iso-seq data from liver, hypothalamus, and ovary. In total a 1,123,996,003 bp genome was generated, including 31 chromosomes assembled in complete scaffolds along with other, unplaced scaffolds. This high-quality genome assembly offers an important genomic resource for the research community using the white-crowned sparrow as a model for understanding avian genome biology and development, and provides a genomic basis for future studies, both fundamental and applied.

Chromosome-Level Genome Assembly and Annotation of a Periodical Cicada Species: Magicicada septendecula

We present a high-quality assembly and annotation of the periodical cicada species, Magicicada septendecula (Hemiptera: Auchenorrhyncha: Cicadidae). Periodical cicadas have a significant ecological impact, serving as a food source for many mammals, reptiles, and birds. Magicicada are well known for their massive emergences of 1 to 3 species that appear in different locations in the eastern United States nearly every year. These year classes ("broods") emerge dependably every 13 or 17 yr in a given location. Recently, it has become clear that 4-yr early or late emergences of a sizeable portion of a population are an important part of the history of brood formation; however, the biological mechanisms by which they track the passage of time remain a mystery. Using PacBio HiFi reads in conjunction with Hi-C proximity ligation data, we have assembled and annotated the first whole genome for a periodical cicada, an important resource for future phylogenetic and comparative genomic analysis. This also represents the first quality genome assembly and annotation for the Hemipteran superfamily Cicadoidea. With a scaffold N50 of 518.9 Mb and a complete BUSCO score of 96.7%, we are confident that this assembly will serve as a vital resource toward uncovering the genomic basis of periodical cicadas' long, synchronized life cycles and will provide a robust framework for further investigations into these insects.

Soluble PD-L1 as a diagnostic and prognostic biomarker in resectable gastric cancer patients

BACKGROUND

In this study, we compared programmed death-ligand 1 (PD-L1) expression in primary tissue samples and its soluble form (sPD-L1) concentration in matched preoperative plasma samples from gastric cancer patients to understand the relationship between tissue and plasma PD-L1 expression and to determine its diagnostic and prognostic value.

METHODS

PD-L1 expression in tissue was assessed by immunohistochemistry and enzyme-linked immunosorbent assay (ELISA), and sPD-L1 concentration in plasma was quantified by ELISA. The levels of the CD274 gene, which encodes for PD-L1 protein, were examined as part of bulk tissue RNA-sequencing analyses. Additionally, we evaluated the association between sPD-L1 levels and various laboratory parameters, disease characteristics, and patient outcomes.

RESULTS

GC patients had significantly higher levels of sPD-L1 in their plasma (71.69 pg/mL) compared to healthy controls (35.34 pg/mL) (p < 0.0001). Moreover, sPD-L1 levels were significantly correlated with tissue PD-L1 protein, CD274 mRNA expression, larger tumor size, advanced tumor stage, and lymph node metastasis. Elevated sPD-L1 levels (> 103.5 ng/mL) were associated with poor overall survival (HR = 2.16, 95%CI 1.15-4.08, p = 0.017). Furthermore, intratumoral neutrophil and dendritic cell levels were directly correlated with plasma sPD-L1 concentration in the GC patients.

CONCLUSIONS

sPD-L1 was readily measurable in GC patients, and its level was associated with GC tissue PD-L1 expression, greater inflammatory cell infiltration, disease progression, and survival. Thus, sPD-L1 may be a useful minimally invasive diagnostic and prognostic biomarker in GC patients.

Long noncoding RNA study: Genome-wide approaches

Long noncoding RNAs (lncRNAs) have been confirmed to play a crucial role in various biological processes across several species. Though many efforts have been devoted to the expansion of the lncRNAs landscape, much about lncRNAs is still unknown due to their great complexity. The development of high-throughput technologies and the constantly improved bioinformatic methods have resulted in a rapid expansion of lncRNA research and relevant databases. In this review, we introduced genome-wide research of lncRNAs in three parts: (i) novel lncRNA identification by high-throughput sequencing and computational pipelines; (ii) functional characterization of lncRNAs by expression atlas profiling, genome-scale screening, and the research of cancer-related lncRNAs; (iii) mechanism research by large-scale experimental technologies and computational analysis. Besides, primary experimental methods and bioinformatic pipelines related to these three parts are summarized. This review aimed to provide a comprehensive and systemic overview of lncRNA genome-wide research strategies and indicate a genome-wide lncRNA research system.

The Interaction of Borrelia burgdorferi with Human Dendritic Cells: Functional Implications

Dendritic cells bridge the innate and adaptive immune responses by serving as sensors of infection and as the primary APCs responsible for the initiation of the T cell response against invading pathogens. The naive T cell activation requires the following three key signals to be delivered from dendritic cells: engagement of the TCR by peptide Ags bound to MHC molecules (signal 1), engagement of costimulatory molecules on both cell types (signal 2), and expression of polarizing cytokines (signal 3). Initial interactions between Borrelia burgdorferi, the causative agent of Lyme disease, and dendritic cells remain largely unexplored. To address this gap in knowledge, we cultured live B. burgdorferi with monocyte-derived dendritic cells (mo-DCs) from healthy donors to examine the bacterial immunopeptidome associated with HLA-DR. In parallel, we examined changes in the expression of key costimulatory and regulatory molecules as well as profiled the cytokines released by dendritic cells when exposed to live spirochetes. RNA-sequencing studies on B. burgdorferi-pulsed dendritic cells show a unique gene expression signature associated with B. burgdorferi stimulation that differs from stimulation with lipoteichoic acid, a TLR2 agonist. These studies revealed that exposure of mo-DCs to live B. burgdorferi drives the expression of both pro- and anti-inflammatory cytokines as well as immunoregulatory molecules (e.g., PD-L1, IDO1, Tim3). Collectively, these studies indicate that the interaction of live B. burgdorferi with mo-DCs promotes a unique mature DC phenotype that likely impacts the nature of the adaptive T cell response generated in human Lyme disease.

The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens

Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.

Assembly of the 81.6 Mb centromere of pea chromosome 6 elucidates the structure and evolution of metapolycentric chromosomes

Centromeres in the legume genera Pisum and Lathyrus exhibit unique morphological characteristics, including extended primary constrictions and multiple separate domains of centromeric chromatin. These so-called metapolycentromeres resemble an intermediate form between monocentric and holocentric types, and therefore provide a great opportunity for studying the transitions between different types of centromere organizations. However, because of the exceedingly large and highly repetitive nature of metapolycentromeres, highly contiguous assemblies needed for these studies are lacking. Here, we report on the assembly and analysis of a 177.6 Mb region of pea (Pisum sativum) chromosome 6, including the 81.6 Mb centromere region (CEN6) and adjacent chromosome arms. Genes, DNA methylation profiles, and most of the repeats were uniformly distributed within the centromere, and their densities in CEN6 and chromosome arms were similar. The exception was an accumulation of satellite DNA in CEN6, where it formed multiple arrays up to 2 Mb in length. Centromeric chromatin, characterized by the presence of the CENH3 protein, was predominantly associated with arrays of three different satellite repeats; however, five other satellites present in CEN6 lacked CENH3. The presence of CENH3 chromatin was found to determine the spatial distribution of the respective satellites during the cell cycle. Finally, oligo-FISH painting experiments, performed using probes specifically designed to label the genomic regions corresponding to CEN6 in Pisum, Lathyrus, and Vicia species, revealed that metapolycentromeres evolved via the expansion of centromeric chromatin into neighboring chromosomal regions and the accumulation of novel satellite repeats. However, in some of these species, centromere evolution also involved chromosomal translocations and centromere repositioning.

Tool and techniques study to plant microbiome current understanding and future needs: an overview

Microorganisms are present in the universe and they play role in beneficial and harmful to human life, society, and environments. Plant microbiome is a broad term in which microbes are present in the rhizo, phyllo, or endophytic region and play several beneficial and harmful roles with the plant. To know of these microorganisms, it is essential to be able to isolate purification and identify them quickly under laboratory conditions. So, to improve the microbial study, several tools and techniques such as microscopy, rRNA, or rDNA sequencing, fingerprinting, probing, clone libraries, chips, and metagenomics have been developed. The major benefits of these techniques are the identification of microbial community through direct analysis as well as it can apply in situ. Without tools and techniques, we cannot understand the roles of microbiomes. This review explains the tools and their roles in the understanding of microbiomes and their ecological diversity in environments.

The mechanistic target of rapamycin complex 1 pathway involved in hepatic gluconeogenesis through peroxisome-proliferator-activated receptor γ coactivator-1α

Cattle can efficiently perform de novo generation of glucose through hepatic gluconeogenesis to meet post-weaning glucose demand. Substantial evidence points to cattle and non-ruminant animals being characterized by phylogenetic features in terms of their differing capacity for hepatic gluconeogenesis, a process that is highly efficient in cattle yet the underlying mechanism remains unclear. Here we used a variety of transcriptome data, as well as tissue and cell-based methods to uncover the mechanisms of high-efficiency hepatic gluconeogenesis in cattle. We showed that cattle can efficiently convert propionate into pyruvate, at least partly, via high expression of acyl-CoA synthetase short-chain family member 1 (ACSS1), propionyl-CoA carboxylase alpha chain (PCCA), methylmalonyl-CoA epimerase (MCEE), methylmalonyl-CoA mutase (MMUT), and succinate-CoA ligase (SUCLG2) genes in the liver (P < 0.01). Moreover, higher expression of the rate-limiting enzymes of gluconeogenesis, such as phosphoenolpyruvate carboxykinase (PCK) and fructose 1,6-bisphosphatase (FBP), ensures the efficient operation of hepatic gluconeogenesis in cattle (P < 0.01). Mechanistically, we found that cattle liver exhibits highly active mechanistic target of rapamycin complex 1 (mTORC1), and the expressions of PCCA, MMUT, SUCLG2, PCK, and FBP genes are regulated by the activation of mTORC1 (P < 0.001). Finally, our results showed that mTORC1 promotes hepatic gluconeogenesis in a peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α) dependent manner. Collectively, our results not only revealed an important mechanism responsible for the quantitative differences in the efficiency of hepatic gluconeogenesis in cattle versus non-ruminant animals, but also established that mTORC1 is indeed involved in the regulation of hepatic gluconeogenesis through PGC-1α. These results provide a novel potential insight into promoting hepatic gluconeogenesis through activated mTORC1 in both ruminants and mammals.

TransMeta simultaneously assembles multisample RNA-seq reads

Assembling RNA-seq reads into full-length transcripts is crucial in transcriptomic studies and poses computational challenges. Here we present TransMeta, a simple and robust algorithm that simultaneously assembles RNA-seq reads from multiple samples. TransMeta is designed based on the newly introduced vector-weighted splicing graph model, which enables accurate reconstruction of the consensus transcriptome via incorporating a cosine similarity-based combing strategy and a newly designed label-setting path-searching strategy. Tests on both simulated and real data sets show that TransMeta consistently outperforms PsiCLASS, StringTie2 plus its merge mode, and Scallop plus TACO, the most popular tools, in terms of precision and recall under a wide range of coverage thresholds at the meta-assembly level. Additionally, TransMeta consistently shows superior performance at the individual sample level.

Bookend: precise transcript reconstruction with end-guided assembly

We developed Bookend, a package for transcript assembly that incorporates data from different RNA-seq techniques, with a focus on identifying and utilizing RNA 5' and 3' ends. We demonstrate that correct identification of transcript start and end sites is essential for precise full-length transcript assembly. Utilization of end-labeled reads present in full-length single-cell RNA-seq datasets dramatically improves the precision of transcript assembly in single cells. Finally, we show that hybrid assembly across short-read, long-read, and end-capture RNA-seq datasets from Arabidopsis thaliana, as well as meta-assembly of RNA-seq from single mouse embryonic stem cells, can produce reference-quality end-to-end transcript annotations.

Characterization and clustering of kinase isoform expression in metastatic melanoma

Mutations to the human kinome are known to play causal roles in cancer. The kinome regulates numerous cell processes including growth, proliferation, differentiation, and apoptosis. In addition to aberrant expression, aberrant alternative splicing of cancer-driver genes is receiving increased attention as it could lead to loss or gain of functional domains, altering a kinase's downstream impact. The present study quantifies changes in gene expression and isoform ratios in the kinome of metastatic melanoma cells relative to primary tumors. We contrast 538 total kinases and 3,040 known kinase isoforms between 103 primary tumor and 367 metastatic samples from The Cancer Genome Atlas (TCGA). We find strong evidence of differential expression (DE) at the gene level in 123 kinases (23%). Additionally, of the 468 kinases with alternative isoforms, 60 (13%) had significant difference in isoform ratios (DIR). Notably, DE and DIR have little correlation; for instance, although DE highlights enrichment in receptor tyrosine kinases (RTKs), DIR identifies altered splicing in non-receptor tyrosine kinases (nRTKs). Using exon junction mapping, we identify five examples of splicing events favored in metastatic samples. We demonstrate differential apoptosis and protein localization between SLK isoforms in metastatic melanoma. We cluster isoform expression data and identify subgroups that correlate with genomic subtypes and anatomic tumor locations. Notably, distinct DE and DIR patterns separate samples with BRAF hotspot mutations and (N/K/H)RAS hotspot mutations, the latter of which lacks effective kinase inhibitor treatments. DE in RAS mutants concentrates in CMGC kinases (a group including cell cycle and splicing regulators) rather than RTKs as in BRAF mutants. Furthermore, isoforms in the RAS kinase subgroup show enrichment for cancer-related processes such as angiogenesis and cell migration. Our results reveal a new approach to therapeutic target identification and demonstrate how different mutational subtypes may respond differently to treatments highlighting possible new driver events in cancer.

Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data

The accuracy of methods for assembling transcripts from short-read RNA sequencing data is limited by the lack of long-range information. Here we introduce Ladder-seq, an approach that separates transcripts according to their lengths before sequencing and uses the additional information to improve the quantification and assembly of transcripts. Using simulated data, we show that a kallisto algorithm extended to process Ladder-seq data quantifies transcripts of complex genes with substantially higher accuracy than conventional kallisto. For reference-based assembly, a tailored scheme based on the StringTie2 algorithm reconstructs a single transcript with 30.8% higher precision than its conventional counterpart and is more than 30% more sensitive for complex genes. For de novo assembly, a similar scheme based on the Trinity algorithm correctly assembles 78% more transcripts than conventional Trinity while improving precision by 78%. In experimental data, Ladder-seq reveals 40% more genes harboring isoform switches compared to conventional RNA sequencing and unveils widespread changes in isoform usage upon m6A depletion by Mettl14 knockout.

Studying the virome in psychiatric disease

An overlooked aspect of current microbiome studies is the role of viruses in human health. Compared to bacterial studies, laboratory and analytical methods to study the entirety of viral communities in clinical samples are rudimentary and need further refinement. In order to address this need, we developed Virobiome-Seq, a sequence capture method and an accompanying bioinformatics analysis pipeline, that identifies viral reads in human samples. Virobiome-Seq is able to enrich for and detect multiple types of viruses in human samples, including novel subtypes that diverge at the sequence level. In addition, Virobiome-Seq is able to detect RNA transcripts from DNA viruses and may provide a sensitive method for detecting viral activity in vivo. Since Virobiome-Seq also yields the viral sequence, it makes it possible to investigate associations between viral genotype and psychiatric illness. In this proof of concept study, we detected HIV1, Torque Teno, Pegi, Herpes and Papilloma virus sequences in Peripheral Blood Mononuclear Cells, plasma and stool samples collected from individuals with psychiatric disorders. We also detected the presence of numerous novel circular RNA viruses but were unable to determine whether these viruses originate from the sample or represent contaminants. Despite this challenge, we demonstrate that our knowledge of viral diversity is incomplete and opportunities for novel virus discovery exist. Virobiome-Seq will enable a more sophisticated analysis of the virome and has the potential of uncovering complex interactions between viral activity and psychiatric disease.

Ryūtō: Improved multi-sample transcript assembly for differential transcript expression analysis and more

MOTIVATION

Accurate assembly of RNA-seq is a crucial step in many analytic tasks such as gene annotation or expression studies. Despite ongoing research, progress on traditional single sample assembly has brought no major breakthrough. Multi-sample RNA-Seq experiments provide more information than single sample datasets and thus constitute a promising area of research. Yet, this advantage is challenging to utilize due to the large amount of accumulating errors.

RESULTS

We present an extension to Ryūtō enabling the reconstruction of consensus transcriptomes from multiple RNA-seq data sets, incorporating consensus calling at low level features. We report stable improvements already at 3 replicates. Ryūtō outperforms competing approaches, providing a better and user-adjustable sensitivity-precision trade-off. Ryūtō's unique ability to utilize a (incomplete) reference for multi sample assemblies greatly increases precision. We demonstrate benefits for differential expression analysis.

CONCLUSION

Ryūtō consistently improves assembly on replicates of the same tissue independent of filter settings, even when mixing conditions or time series. Consensus voting in Ryūtō is especially effective at high precision assembly, while Ryūtō's conventional mode can reach higher recall.

AVAILABILITY

Ryūtō is available at https://github.com/studla/RYUTO.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

FINDER: an automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

BACKGROUND

Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative.

RESULTS

We present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species.

CONCLUSIONS

FINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision-ideal for bench researchers with limited experience in handling computational tools.

Insights Into the Albinism Mechanism for Two Distinct Color Morphs of Northern Snakehead, Channa argus Through Histological and Transcriptome Analyses

The great northern snakehead (Channa argus) is one of the most important economic and conservational fish in China. In this study, the melanocytes in the skin of two distinct color morphs C. argus were investigated and compared through employment of the microscopic analysis, hematoxylin and eosin (H&E) and Masson Fontana staining. Our results demonstrated the uneven distribution of melanocytes with extremely low density and most of them were in the state of aging or death. Meanwhile, there was no obvious pigment layer and melanocytes distribution pattern found in the albino-type (AT), while the melanocytes were evenly distributed with abundance in the bicolor-type (BT). The transcriptome analysis through Illumina HiSeq sequencing showed that a total of 34.93 Gb Clean Data was obtained, and Q30 base percentage reached 92.66%. The BT and AT northern snakeheads transcriptome data included a total of 56,039,701 and 60,410,063 clean reads (n = 3), respectively. In gene expression analyses, the sample correlation coefficients (r) were ranged between 0.92 and 1.00; the contribution of PC1 and PC2 were 50.25 and 13.73% by using PCA cluster analysis, the total number of DEGs were 1024 (559 up-regulated and 465 down-regulated), and the number of annotated DEGs was 767 (COG 172, KEGG 262, GO 288, SwissProt 548, Pfam 579 and NR 765). Additionally, 46,363 ± 873 and 44,947 ± 392 single nucleotide polymorphisms (SNPs) were compiled via genetic structure analysis, respectively. Ten key pigment-related genes were screened using qRT-PCR. And all of them revealed extremely higher expression levels in the skin of BT than those of AT. This is the first study to analyze the mechanism of albino characteristics of Channa via histology and transcriptomics, and also provide the oretical and practical support for the protection and development of germplasm resources for C. argus.