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1. AnchorWave: Sensitive alignment of genomes with high sequence diversity, extensive structural polymorphism, and whole-genome duplication

   https://doi.org/10.1073/pnas.2113075119  

   Song, Baoxing; Marco-Sola, Santiago; Moreto, Miquel; Johnson, Lynn; Buckler, Edward S.; Stitzer, Michelle C. (January 2022, Proceedings of the National Academy of Sciences)

   Millions of species are currently being sequenced, and their genomes are being compared. Many of them have more complex genomes than model systems and raise novel challenges for genome alignment. Widely used local alignment strategies often produce limited or incongruous results when applied to genomes with dispersed repeats, long indels, and highly diverse sequences. Moreover, alignment using many-to-many or reciprocal best hit approaches conflicts with well-studied patterns between species with different rounds of whole-genome duplication. Here, we introduce Anchored Wavefront alignment (AnchorWave), which performs whole-genome duplication–informed collinear anchor identification between genomes and performs base pair–resolved global alignment for collinear blocks using a two-piece affine gap cost strategy. This strategy enables AnchorWave to precisely identify multikilobase indels generated by transposable element (TE) presence/absence variants (PAVs). When aligning two maize genomes, AnchorWave successfully recalled 87% of previously reported TE PAVs. By contrast, other genome alignment tools showed low power for TE PAV recall. AnchorWave precisely aligns up to three times more of the genome as position matches or indels than the closest competitive approach when comparing diverse genomes. Moreover, AnchorWave recalls transcription factor–binding sites at a rate of 1.05- to 74.85-fold higher than other tools with significantly lower false-positive alignments. AnchorWave complements available genome alignment tools by showing obvious improvement when applied to genomes with dispersed repeats, active TEs, high sequence diversity, and whole-genome duplication variation. 

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2. Combined analysis of transposable elements and structural variation in maize genomes reveals genome contraction outpaces expansion

   https://doi.org/10.1371/journal.pgen.1011086  

   Munasinghe, Manisha; Read, Andrew; Stitzer, Michelle C.; Song, Baoxing; Menard, Claire C.; Ma, Kristy Yubo; Brandvain, Yaniv; Hirsch, Candice N.; Springer, Nathan (December 2023, PLOS Genetics)

   VITTE, Clémentine (Ed.)

   Structural differences between genomes are a major source of genetic variation that contributes to phenotypic differences. Transposable elements, mobile genetic sequences capable of increasing their copy number and propagating themselves within genomes, can generate structural variation. However, their repetitive nature makes it difficult to characterize fine-scale differences in their presence at specific positions, limiting our understanding of their impact on genome variation. Domesticated maize is a particularly good system for exploring the impact of transposable element proliferation as over 70% of the genome is annotated as transposable elements. High-quality transposable element annotations were recently generated forde novogenome assemblies of 26 diverse inbred maize lines. We generated base-pair resolved pairwise alignments between the B73 maize reference genome and the remaining 25 inbred maize line assemblies. From this data, we classified transposable elements as either shared or polymorphic in a given pairwise comparison. Our analysis uncovered substantial structural variation between lines, representing both simple and complex connections between TEs and structural variants. Putative insertions in SNP depleted regions, which represent recently diverged identity by state blocks, suggest some TE families may still be active. However, our analysis reveals that within these recently diverged genomic regions, deletions of transposable elements likely account for more structural variation events and base pairs than insertions. These deletions are often large structural variants containing multiple transposable elements. Combined, our results highlight how transposable elements contribute to structural variation and demonstrate that deletion events are a major contributor to genomic differences. 

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3. RelocaTE2: a high resolution transposable element insertion site mapping tool for population resequencing

   https://doi.org/10.7717/peerj.2942  

   Chen, Jinfeng; Wrightsman, Travis R.; Wessler, Susan R.; Stajich, Jason E. (January 2017, PeerJ)

   Background Transposable element (TE) polymorphisms are important components of population genetic variation. The functional impacts of TEs in gene regulation and generating genetic diversity have been observed in multiple species, but the frequency and magnitude of TE variation is under appreciated. Inexpensive and deep sequencing technology has made it affordable to apply population genetic methods to whole genomes with methods that identify single nucleotide and insertion/deletion polymorphisms. However, identifying TE polymorphisms, particularly transposition events or non-reference insertion sites can be challenging due to the repetitive nature of these sequences, which hamper both the sensitivity and specificity of analysis tools. Methods We have developed the tool RelocaTE2 for identification of TE insertion sites at high sensitivity and specificity. RelocaTE2 searches for known TE sequences in whole genome sequencing reads from second generation sequencing platforms such as Illumina. These sequence reads are used as seeds to pinpoint chromosome locations where TEs have transposed. RelocaTE2 detects target site duplication (TSD) of TE insertions allowing it to report TE polymorphism loci with single base pair precision. Results and Discussion The performance of RelocaTE2 is evaluated using both simulated and real sequence data. RelocaTE2 demonstrate high level of sensitivity and specificity, particularly when the sequence coverage is not shallow. In comparison to other tools tested, RelocaTE2 achieves the best balance between sensitivity and specificity. In particular, RelocaTE2 performs best in prediction of TSDs for TE insertions. Even in highly repetitive regions, such as those tested on rice chromosome 4, RelocaTE2 is able to report up to 95% of simulated TE insertions with less than 0.1% false positive rate using 10-fold genome coverage resequencing data. RelocaTE2 provides a robust solution to identify TE insertion sites and can be incorporated into analysis workflows in support of describing the complete genotype from light coverage genome sequencing. 

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4. Chromosome-scale genome assemblies and annotations for Poales species Carex cristatella , Carex scoparia , Juncus effusus , and Juncus inflexus

   https://doi.org/10.1093/g3journal/jkac211  

   Planta, Jose; Liang, Yu-Ya; Xin, Haoyang; Chansler, Matthew T; Prather, L Alan; Jiang, Ning; Jiang, Jiming; Childs, Kevin L (August 2022, G3 Genes|Genomes|Genetics)

   Tribble, C (Ed.)

   Abstract The majority of sequenced genomes in the monocots are from species belonging to Poaceae, which include many commercially important crops. Here, we expand the number of sequenced genomes from the monocots to include the genomes of 4 related cyperids: Carex cristatella and Carex scoparia from Cyperaceae and Juncus effusus and Juncus inflexus from Juncaceae. The high-quality, chromosome-scale genome sequences from these 4 cyperids were assembled by combining whole-genome shotgun sequencing of Nanopore long reads, Illumina short reads, and Hi-C sequencing data. Some members of the Cyperaceae and Juncaceae are known to possess holocentric chromosomes. We examined the repeat landscapes in our sequenced genomes to search for potential repeats associated with centromeres. Several large satellite repeat families, comprising 3.2–9.5% of our sequenced genomes, showed dispersed distribution of large satellite repeat clusters across all Carex chromosomes, with few instances of these repeats clustering in the same chromosomal regions. In contrast, most large Juncus satellite repeats were clustered in a single location on each chromosome, with sporadic instances of large satellite repeats throughout the Juncus genomes. Recognizable transposable elements account for about 20% of each of the 4 genome assemblies, with the Carex genomes containing more DNA transposons than retrotransposons while the converse is true for the Juncus genomes. These genome sequences and annotations will facilitate better comparative analysis within monocots. 

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5. ExplorATE: a new pipeline to explore active transposable elements from RNA-seq data

   https://doi.org/10.1093/bioinformatics/btac354  

   Femenias, Martin M.; Santos, Juan C.; Sites, Jr, Jack W.; Avila, Luciano J.; Morando, Mariana; Kendziorski, ed., Christina (May 2022, Bioinformatics)

   Abstract MotivationTransposable elements (TEs) are ubiquitous in genomes and many remain active. TEs comprise an important fraction of the transcriptomes with potential effects on the host genome, either by generating deleterious mutations or promoting evolutionary novelties. However, their functional study is limited by the difficulty in their identification and quantification, particularly in non-model organisms. ResultsWe developed a new pipeline [explore active transposable elements (ExplorATE)] implemented in R and bash that allows the quantification of active TEs in both model and non-model organisms. ExplorATE creates TE-specific indexes and uses the Selective Alignment (SA) to filter out co-transcribed transposons within genes based on alignment scores. Moreover, our software incorporates a Wicker-like criteria to refine a set of target TEs and avoid spurious mapping. Based on simulated and real data, we show that the SA strategy adopted by ExplorATE achieved better estimates of non-co-transcribed elements than other available alignment-based or mapping-based software. ExplorATE results showed high congruence with alignment-based tools with and without a reference genome, yet ExplorATE required less execution time. Likewise, ExplorATE expands and complements most previous TE analyses by incorporating the co-transcription and multi-mapping effects during quantification, and provides a seamless integration with other downstream tools within the R environment. Availability and implementationSource code is available at https://github.com/FemeniasM/ExplorATEproject and https://github.com/FemeniasM/ExplorATE_shell_script. Data available on request. Supplementary informationSupplementary data are available at Bioinformatics online. 

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