Journal: bioRxiv

Article Title: Myc and Kras cooperate in adult acinar cells to drive phenotypic heterogeneity, metastasis, and therapeutic resistance in a novel pancreatic cancer mouse model

doi: 10.1101/2025.07.14.664767

Figure Lengend Snippet: A. Principal components analysis of autochthonous KMC, KM/+C, and KPC tumors cluster into three distinctive groups B. Scatter plot with regression line demonstrating correlation between normalized Kras expression and Myc expression in murine tumors (Pearson r correlation coefficient = 0.74), with grouping of transcriptional clustering, irrespective of genotype. C. Heatmap demonstrating sample clustering by GSVA scoring of hallmark gene sets including several curated PDAc Kras specific signatures. Annotation includes copy number alteration for Kras and Myc, transcriptional clustering, PurIST subtyping by shrunken centroid, and model genotype. D. Copy number alteration plots of KMC mice from low pass whole genome sequencing, separated by transcriptional Cluster demonstrating diverse patterns of chromosomal instability in the KMC model; annotated with approximate location of canonical PDAc drivers and KMC alleles.

Article Snippet: Low-pass whole genome sequencing was performed on tumor/normal pairs first by aligning short- read sequencing data with BWA mem with default settings., Second, ichorCNA ( https://github.com/broadinstitute/ichorCNA , Broad Institute, Cambridge, MA) was used to predict CNVs and estimate tumor fraction with default settings.

Techniques: Expressing, Sequencing

Journal: bioRxiv

Article Title: Myc and Kras cooperate in adult acinar cells to drive phenotypic heterogeneity, metastasis, and therapeutic resistance in a novel pancreatic cancer mouse model

doi: 10.1101/2025.07.14.664767

Figure Lengend Snippet: A. A circular dendrogram of primary human PDAc, KMC, and KPC tumors subjected to RNA sequencing, normalization, homolog mapping, and ComBat normalization. Histogram is by unsupervised Ward D2 clustering. Annotation rings (inner to outermost) describe receipt of chemotherapy (neoadjuvant = pink, adjuvant = blue), pORG score tertile, single-sample GSEA Myc V1 targets score, and survival quartile. Outer ring denotes subject number, colored for humans by PurIST subtyping (blue = basal-like, red = classical) and for mice by genotype (green = KMC, light blue = KM/+C, orange = KPC). The KMC transcriptional clusters (cf. fig 5A) are annotated.

Article Snippet: Low-pass whole genome sequencing was performed on tumor/normal pairs first by aligning short- read sequencing data with BWA mem with default settings., Second, ichorCNA ( https://github.com/broadinstitute/ichorCNA , Broad Institute, Cambridge, MA) was used to predict CNVs and estimate tumor fraction with default settings.

Techniques: RNA Sequencing, Adjuvant

Journal: Science Advances

Article Title: Transcriptional readthrough precedes alternative splicing programs triggered in CML cells by imatinib

doi: 10.1126/sciadv.aea2475

Figure Lengend Snippet: ( A ) Change in SPI values from short-read sequencing of mRNA in IM 1h (top) or 18 h (bottom) relative to CTRL. Introns ( n = ~35,300) detected by at least 500 reads and with significant changes in SPI (adjusted P value < 0.05) are marked in blue (20 in IM 1h and 193 in IM 18h). Adjusted P values were determined using chi-square tests for pairs of replicates and the Benjamini-Hochberg procedure. ( B ) Significant changes in alternative splicing in cells treated with JTE or IM 18h. A3SS or A5SS, alternative 3′ or 5′ splice site; MXE, mutually exclusive events. Number of significant events [false discovery rate (FDR) < 0.05] detected by at least 20 reads and with absolute inclusion level difference to CTRL > 0.1. ( C ) Comparison of genes with RTI ≥ 0.2 (RTI) and genes with significant changes in RI and CE at the level of nuclear RNA in IM 18h. Only single intersections with RTI are presented. ( D ) Comparison of genes with significant changes in RI (left) or CE (right) in IM or JTE 18h. ( E ) Sashimi plot for EIF4H (left) and quantification of isoform usage (right) in CTRL versus JTE or IM 18h. Isoforms 1 and 2 represent the skipping and inclusion, respectively, of exon 5 (beige highlight). Percentage of transcripts belonging to each isoform in three replicates (means ± SEM). Significance based on two-tailed Student’s t test (* P < 0.05; *** P < 0.001).

Article Snippet: Long- and short-read sequencing raw data are available through the National Center for Biotechnology Information (NCBI) under GEO accession numbers: GSE283849 , GSE283813 , and GSE310243 .

Techniques: Sequencing, Alternative Splicing, Comparison, Two Tailed Test

Journal: Science Advances

Article Title: Transcriptional readthrough precedes alternative splicing programs triggered in CML cells by imatinib

doi: 10.1126/sciadv.aea2475

Figure Lengend Snippet: ( A ) Examples of reads aligned to RBM14 and RBM4 from long- and short-read sequencing of IM-treated cells. Chimeric reads marked in blue. ( B ) Sashimi plot for RBM14 and RBM4 (chromosome 11: 66616624 to 66644972) based on a single representative CTRL replicate. Only splicing events detected more than five times are shown. Black represents read coverage density in RPKM, gray lines represent splicing events within gene bodies, and the red line represents chimeric splicing. ( C ) Scheme of primer design for real-time PCR semiquantification (qPCR) of chimera expression. ( D ) Expression of selected chimeras [red primers in (C)] in CD34 + -enriched cells from three healthy donors (HD) and two patients with CML (CML), normalized to spike-in controls and presented relative to the expression level of the last exon in the upstream gene [beige primers in (C)]. ( E ) Comparison of readthrough chimeras identified by SOAPfuse analysis of mRNA-seq for CTRL or IM-treated K562 cells. ( F ) Changes in chimera transcript expression normalized to spike-in control referenced to the last exon in the upstream gene and expressed as fold change upon IM treatment. Level in CTRL as 1.0, marked with the dashed line. Significance based on two-way ANOVA test (* P < 0.05; ** P < 0.005; *** P < 0.001).

Article Snippet: Long- and short-read sequencing raw data are available through the National Center for Biotechnology Information (NCBI) under GEO accession numbers: GSE283849 , GSE283813 , and GSE310243 .

Techniques: Sequencing, Real-time Polymerase Chain Reaction, Expressing, Comparison, Control

Journal: bioRxiv

Article Title: Long-read sequencing of trios reveals increased germline and postzygotic mutation rates in repetitive DNA

doi: 10.1101/2025.07.18.665621

Figure Lengend Snippet: A. Example IGV views of a germline and postzygotic mutation in HiFi read data. B. The number of autosomal de novo germline and postzygotic SNVs, insertions and deletions <50 bp, and tandem repeat mutations observed in each sample. Sibling pairs are grouped by family and highlighted in blue, with the proband above the sibling. C. Upset plot of origin assignment shows concordance between HiFi haplotypes, ONT haplotypes, and allele balance. D. Allele balance distribution for autosomal germline and postzygotic SNVs across PacBio HiFi, Illumina, and ONT read data. E. Distribution of the size of autosomal insertions, deletions, and tandem repeat mutations.

Article Snippet: We leveraged long-read Pacific Biosciences high-fidelity (HiFi) sequencing data derived from blood for variant discovery, and both long-read Oxford Nanopore Technologies (ONT) and short-read Illumina data for validation purposes.

Techniques: Mutagenesis

Journal: bioRxiv

Article Title: Topsicle: a method for estimating telomere length from whole genome long-read sequencing data

doi: 10.1101/2025.07.10.664126

Figure Lengend Snippet: Analysis of telomere repeat k-mers in long read sequences from A. thaliana Col-0. (A) Example Type I, II, and III reads from A. thaliana Col-0 Nanopore reads. Each color represents an exact match to the telomere repeat AAACCCT and the position found across the sequencing read. Reads are shown so the 5’ end of the sequence is enriched for the AAACCCT repeat and reads with 3’ end enriched for the complement sequence TTTGGGA we show the reverse complement sequence. (B) Co-occurrence heatmap displaying the frequency of a telomere repeat 5-mer (original telomere repeat sequence is AAACCCT) with all possible dinucleotide sequences that can be found at the end of the 5-mer. Top shows frequencies from analyzing reads aligning to chromosome 1R for Nanopore reads and bottom show frequencies from PacBio reads aligning to chromosome 1R. (C) An example Type III Nanopore sequencing read displaying the occurrence of a 4-mer and 5-mer of the original telomere repeat sequence AAACCCT (Top). A sliding window analysis where each window is size 100 bp and it slides 7 bp (bottom). In the window the average k-mer count is calculated and a change point detection method is applied to determine the window where there is a drop in telomere repeat count (red dotted line). (D) Telomere length estimates for Nanopore sequencing reads aligning to chromosome 1L and 1R.

Article Snippet: Methods such as TelSeq [ ], K-seek [ , ], Computel [ ], Telomerecat [ ], and TelomereHunter [ ] have been used to analyze short read sequencing data (usually generated from the Illumina sequencing platform) for estimating the telomere length of a sample using an approach analogous to a qPCR based method.

Techniques: Sequencing, Nanopore Sequencing

Journal: bioRxiv

Article Title: Topsicle: a method for estimating telomere length from whole genome long-read sequencing data

doi: 10.1101/2025.07.10.664126

Figure Lengend Snippet: Distribution of Telomere Repeat Count (TRC) values from raw long sequences. (A) TRC values for A. thaliana Col-0 Nanopore reads (ERR11436636) that were visually categorized as Type I, II, or III reads. TRC values were calculated using the 4-mers from the telomere repeat sequence AAACCCT. (B) TRC values for A. thaliana Col-0 Nanopore reads aligning to chromosome ends or from all sequencing reads. TRC values were calculated using the 4-mers from the telomere repeat sequence AAACCCT. (C) TRC values for maize B73 PacBio reads aligning to chromosome ends or from all sequencing reads. For maize the TRC values were calculated using the reads from a single sequencing library (ERR3288278) out of a total 18 libraries sequenced for B73. TRC values were calculated using the 4-mers from the telomere repeat sequence AAACCCT. (D) TRC values for Maize B73 PacBio reads aligning to chromosome ends or from all sequencing reads. TRC values were calculated using the 5-mers from the telomere repeat sequence AAACCCT. (E) Telomere length estimates from the A. thaliana Col-0 Nanopore reads using various TRC value cutoff and k-mer sizes. (F) Telomere length estimates from the maize B73 PacBio reads using various TRC value cutoff and k-mer sizes.

Article Snippet: Methods such as TelSeq [ ], K-seek [ , ], Computel [ ], Telomerecat [ ], and TelomereHunter [ ] have been used to analyze short read sequencing data (usually generated from the Illumina sequencing platform) for estimating the telomere length of a sample using an approach analogous to a qPCR based method.

Techniques: Sequencing

Journal: bioRxiv

Article Title: Topsicle: a method for estimating telomere length from whole genome long-read sequencing data

doi: 10.1101/2025.07.10.664126

Figure Lengend Snippet: Overview of Topsicle estimating telomere length from long read sequencing data.

Article Snippet: Methods such as TelSeq [ ], K-seek [ , ], Computel [ ], Telomerecat [ ], and TelomereHunter [ ] have been used to analyze short read sequencing data (usually generated from the Illumina sequencing platform) for estimating the telomere length of a sample using an approach analogous to a qPCR based method.

Techniques: Sequencing

Journal: bioRxiv

Article Title: Topsicle: a method for estimating telomere length from whole genome long-read sequencing data

doi: 10.1101/2025.07.10.664126

Figure Lengend Snippet: Applying Topsicle on simulated dataset. (A) Telomere length was estimated with Topsicle using the 4-mer and TRC value cutoff of 0.4 on 30 simulated reads with error rates of 10%, 20%, and 30% for reads with varying length and proportion of telomere repeat. For each simulation the read length is indicated on the right side of the bar (“|”) and the length of the telomere repeat is on the left side of the bar. (B) Coverage simulation by random sampling the whole genome sequencing data and using Topsicle to estimate telomere length. For each coverage the random sampling was done 20 times and Topsicle was used to estimate the telomere length using the 4-mer telomere repeat and TRC value cutoff of 0.4 to select for telomere reads. Each point represents the median telomere length from the sampled dataset.

Article Snippet: Methods such as TelSeq [ ], K-seek [ , ], Computel [ ], Telomerecat [ ], and TelomereHunter [ ] have been used to analyze short read sequencing data (usually generated from the Illumina sequencing platform) for estimating the telomere length of a sample using an approach analogous to a qPCR based method.

Techniques: Sampling, Sequencing

Journal: bioRxiv

Article Title: Topsicle: a method for estimating telomere length from whole genome long-read sequencing data

doi: 10.1101/2025.07.10.664126

Figure Lengend Snippet: Application of Topsicle on A. thaliana long read sequencing dataset. (A) Distribution of telomere length estimates from analyzing whole genome long read sequencing data of 104 A. thaliana ecotypes. Red dotted line indicates the median (2815 bp). (B) Scatter plot of telomere lengths from 31 ecotypes that have telomere length estimates from TRF and Topsicle. Pearson’s r is shown in the upper-left side and the line of best fit is shown in red line (TRF telomere length = 1.71 ξ Topsicle telomere length – 1118.13). (C) Boxplot of chromosome specific telomere lengths across the 104 A. thaliana ecotypes. Note for chromosome 2L and 4L the majority of the ecotypes did not have reads aligning to those chromosome ends, which prevented the estimation of their chromosome specific telomere length with Topsicle.

Article Snippet: Methods such as TelSeq [ ], K-seek [ , ], Computel [ ], Telomerecat [ ], and TelomereHunter [ ] have been used to analyze short read sequencing data (usually generated from the Illumina sequencing platform) for estimating the telomere length of a sample using an approach analogous to a qPCR based method.

Techniques: Sequencing

Journal: bioRxiv

Article Title: Topsicle: a method for estimating telomere length from whole genome long-read sequencing data

doi: 10.1101/2025.07.10.664126

Figure Lengend Snippet: Application of Topsicle on maize long read sequencing dataset. (A) Telomere length estimates for the 18 PacBio libraries for B73. (B) Telomere length estimates for the 16 PacBio libraries for OH43. (C) Boxplot of telomere length estimates for the 27 maize genotypes using Topsicle. The plot shows the telomere length estimates of reads from all sequencing libraries of a genotype that passed a TRC cutoff of 0.4 calculated using a 5-mer repeat sequence. For the genotypes that were previously analyzed with TRF, the telomere length estimates are indicated with a red diamond.

Article Snippet: Methods such as TelSeq [ ], K-seek [ , ], Computel [ ], Telomerecat [ ], and TelomereHunter [ ] have been used to analyze short read sequencing data (usually generated from the Illumina sequencing platform) for estimating the telomere length of a sample using an approach analogous to a qPCR based method.

Techniques: Sequencing

Journal: bioRxiv

Article Title: Topsicle: a method for estimating telomere length from whole genome long-read sequencing data

doi: 10.1101/2025.07.10.664126

Figure Lengend Snippet: Application of Topsicle on Mimulus Nanopore sequencing data. (A) Co-occurrence heatmap displaying the frequency of a telomere repeat. Top shows the telomere repeat 4-mer (original telomere repeat sequence is 6-mer AAACCG) with all possible dinucleotide sequences that can be found at the end of the 4-mer. Bottom shows the telomere repeat 5-mer (original telomere repeat sequence is 7-mer AAACCCG) with all possible dinucleotide sequences that can be found at the end of the 5-mer. Frequencies were counted from Nanopore reads that aligned to chromosome ends of the reference genomes of each respective species. (B) Boxplot of telomere length estimated from Topsicle. For each species the TRF based telomere length is indicated with a red diamond. Significant differences in Topsicle estimated telomere length after a Mann Whitney U test are indicated with *** (p-value < 0.0001).

Article Snippet: Methods such as TelSeq [ ], K-seek [ , ], Computel [ ], Telomerecat [ ], and TelomereHunter [ ] have been used to analyze short read sequencing data (usually generated from the Illumina sequencing platform) for estimating the telomere length of a sample using an approach analogous to a qPCR based method.

Techniques: Nanopore Sequencing, Sequencing, MANN-WHITNEY