Consensus Sequence

VSEARCH includes commands to perform de novo clustering using a greedy and heuristic centroid-based algorithm with an adjustable sequence similarity threshold specified with the id option (e.g., 0.97). The input sequences are either processed in the user supplied order (cluster_smallmem) or pre-sorted based on length (cluster_fast) or abundance (the new cluster_size option). Each input sequence is then used as a query in a search against an initially empty database of centroid sequences. The query sequence is clustered with the first centroid sequence found with similarity equal to or above the threshold. The search is performed using the heuristic approach described above which generally finds the most similar sequences first. If no matches are found, the query sequence becomes the centroid of a new cluster and is added to the database. If maxaccepts is higher than 1, several centroids with sufficient sequence similarity may be found and considered. By default, the query is clustered with the centroid presenting the highest sequence similarity (distance-based greedy clustering, DGC), or, if the sizeorder option is turned on, the centroid with the highest abundance (abundance-based greedy clustering, AGC) (He et al., 2015 (link); Westcott & Schloss, 2015 (link); Schloss, 2016 ). VSEARCH performs multi-threaded clustering by searching the database of centroid sequences with several query sequences in parallel. If there are any non-matching query sequences giving rise to new centroids, the required internal comparisons between the query sequences are subsequently performed to achieve correct results. For each cluster, VSEARCH can create a simple multiple sequence alignment using the center star method (Gusfield, 1993 (link)) with the centroid as the center sequence, and then compute a consensus sequence and a sequence profile.

The GENCODE gene set is created by merging the results of manual and computational gene annotation methods. Manual gene annotation has two major modes of operation: clone-by-clone and targeted annotation. ‘Clone-by-clone’ annotation involves ‘walking’ across a genomic region, investigating the sequence, aligned expression data and computational predictions for each BAC clone. In doing so, an expert annotator investigates all possible genic features and considers all possible annotations and biotypes simultaneously. We believe this approach carries substantial advantages. For example, the decision to annotate a locus as protein-coding or pseudogenic benefits from being able to weigh both possibilities in light of all available evidence. This process helps prevent false positive and false negative misclassifications. Targeted annotation is designed to answer specific questions such as ‘is there an unannotated protein-coding gene in this position?’ Ranked target lists are generated by computational analysis based, for example, on transcriptomic data, shotgun proteomic data or conservation measures. Over the last two years mouse annotation has been dominated by the clone-by-clone approach while the human genome has been refined entirely via targeted reannotation except for the annotation of human assembly patches and haplotypes released by the Genome Reference Consortium (15 (link)), which take a clone-by-clone approach.
Over the last two years, we have focused on two broad areas: completing the first pass manual annotation across the entire mouse reference genome and a dedicated effort to improve the annotation of protein-coding genes in human and mouse.
We have completed the annotation of novel protein-coding genes, lncRNAs and pseudogenes, plus QC and updating previous annotation where necessary for mouse chromosomes 9, 10, 11, 12, 13, 14, 15, 16 and 17. These updates bring the fraction of the mouse genome with completed first pass manual annotation to approximately 97%. In addition, we have continued to work with the NCBI and Mouse Genome Informatics project at the Jackson Laboratory to resolve annotation differences for protein-coding, pseudogene and lncRNA loci. For protein-coding genes this is under the umbrella of the Consensus Coding Sequence (CCDS) project (16 (link)).
We have also manually investigated unannotated regions of high protein-coding potential identified by whole genome analysis using PhyloCSF (17 (link)) (a tool described in more detail below). In human, this led to the addition of 144 novel protein-coding genes and 271 pseudogenes (of which 42 were unitary pseudogenes). In mouse, we annotated orthologous loci for all but 11 of the 144 human protein-coding genes. We have also revisited the annotation of all olfactory receptor loci in both human and mouse, using RNAseq data to define 5′ and 3′ UTR sequences for ∼1400 loci. In human we have also targeted a ‘deep dive’ manual reannotation of genes on clinical panels for paediatric neurological disorders to identify missing functional alternative splicing. Incorporating second and third generation transcriptomic data, we reannotated ∼190 genes and added more than 3600 alternatively spliced transcripts, including ∼1400 entirely novel exons and an additional ∼30kb of CDS. We have also completed an effort to capture all recently described unannotated microexons (18 (link)) into GENCODE, and further added an additional 146 novel microexons mined from public SLRseq data (19 (link)).
As part of the CCDS collaboration with RefSeq, we have checked a large subset of human loci where there was disagreement over gene biotype. Similarly, we have checked all UniProt manually annotated and reviewed (i.e. Swiss-Prot) accessions that lack an equivalent in GENCODE. As a result, we added 32 novel protein-coding loci to GENCODE and rejected more than 200 putative coding loci. Finally, we are manually reviewing genes previously annotated as protein-coding, but with weak or no support based on a method incorporating UniProt, APPRIS, PhyloCSF, Ensembl comparative genomics, RNA-seq, mass spectrometry and variation data (20 (link),21 (link)). Of the 821 loci investigated to date, 54 have had their coding status removed while a further 110 potentially dubious cases remain under review.
The approach taken reflects in the kinds of updates captured in the annotation. For example, the targeted reannotation in human leads to the annotation of few novel protein-coding loci but many novel transcripts at updated protein-coding and lncRNA loci. Conversely, in mouse the emphasis on clone-by-clone annotation identifies many more novel loci and transcripts across a broader range of biotypes (Figure 1).

NextDenovo v1.0 (https://github.com/Nextomics/NextDenovo) was used with parameters “read_cuoff = 2k; seed_cutoff = 20k; blocksize = 2g” to align the long reads sequenced by Nanopore PromethION platform against themselves for self-correction and comparison of overlapping regions to generate consensus sequences to obtain primary assembled genome sequence information. The genome was then further assembled with corrected reads using wtdbg2 [58 (link)] with parameters “-k 0 -p 19 -S 2 --rescue-low-cov-edges；wtdbg-cns -c 0 -k 11”. After quality control, the short-read data were aligned to the assembled genome using BWA with default parameters. Contigs were polished using NextPolish v1.01 [59 (link)] with three rounds of alignment for long reads, followed by four rounds for short reads. The Hi-C data filtered with fastp v0.20.0 [60 (link)] were then used to correct and assemble the contigs to chromosome-level scaffolds using bowtie2 v2.3.2 [61 (link)] based on the interaction signals by LACHESIS (http://shendurelab.github.io/LACHESIS/). The completeness of the genome was evaluated against vertebrate lineages with Benchmarking Universal Single-Copy Orthologs (BUSCO v3.1.0).

Total RNA was isolated from shells of ETH3 and control in ‘Huashuo’ at the fruit mature stage from field grown plants using the Trizol Reagent Kit (Invitrogen, Carlsbad, USA). The quality of total RNA was evaluated using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, USA). The concentration and purity of each mRNA sample was determined using NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). The construction of the libraries and the RNA-seq were performed by the Biomarker Technologies Co., Ltd (Beijing, China). After removing the adaptor sequences and low-quality reads, high quality clean reads from all samples were assembled using Trinity software (release-2012-10-05) to construct unique consensus sequences for reference (Chen et al., 2019 (link)). These sequences obtained from the trinity assembly were called unigenes. These unigenes were annotated using the BLASTx alignment (E-value ≤ 10^-5) to various public databases (the NCBI nonredundant protein (Nr) database, Kyoto Encyclopedia of Genes and Genomes (KEGG) database, Clusters of Orthologous Group (COG), Swiss-Prot protein database, and Gene Ontology (GO) database). The unigenes expression was calculated according to the reads per kilobase transcriptome per million mapped reads (RPKM) method. Genes showing differences in expression between two samples were identified using DESeq2 software (Love et al., 2014 (link)). Differentially expressed genes (DEGs) were evaluated based on false discovery rate (FDR < 0.05) and fold change (FC ≥ 2). Furthermore, functional enrichment analyses of DEGs including GO functions and KEGG pathways were implemented.