Ant / bacteria co-evolution, landscape genomics, and population genomics
Vertebrate and/or invertebrate genome evolution

If you might be interested in joining our research group, send an email with your intent and why this group would potentially be a good fit for your future goals along with a CV / Resume to jdmanthey (at) gmail (dot) com

More at https://mantheylab.org/

Why Normalize RNA-Seq Data?

Normalization is a crucial step in RNA-Seq analysis for the following reasons:

• Sequencing depth: Different RNA-Seq experiments produce varying numbers of reads, making direct comparisons between samples misleading.

• Gene length: Longer genes inherently generate more reads, irrespective of their actual expression level.

• Bias reduction: Normalization mitigates technical biases, enabling meaningful biological interpretation.

TPM (Transcripts Per Million)

TPM measures the proportion of reads mapped to a transcript, normalized by transcript length and sequencing depth. It is calculated as:

Key Features:

1. Proportionality: TPM values sum to 1,000,000 across all transcripts in a sample, making it easier to compare between samples.

2. Intuitive interpretation: TPM values directly represent the abundance of transcripts in a sample.

3. Preferred for comparisons: TPM facilitates between-sample comparisons better than FPKM.

FPKM (Fragments Per Kilobase Million)

FPKM normalizes read counts by transcript length and sequencing depth, but without enforcing proportionality like TPM. It is defined as:

Key Features:

1. Historical significance: FPKM was one of the first normalization methods used for RNA-Seq.

2. Single-end vs. paired-end: In paired-end sequencing, FPKM becomes RPKM (Reads Per Kilobase Million).

3. Limited utility: FPKM values are not as robust as TPM for cross-sample comparisons due to lack of proportionality.

CPM (Counts Per Million)

CPM normalizes raw read counts by sequencing depth, without considering gene length. It is expressed as:

Key Features:

1. Simplicity: CPM is straightforward and computationally less intensive.

2. Application: Suitable for non-length-dependent analyses, such as comparing total expression levels or differential expression analysis.

3. Gene length agnostic: CPM does not correct for gene length, making it less ideal for measuring expression levels.

When to Use Each Method

• TPM: Best for comparing expression levels between samples, especially when transcript length and sequencing depth vary.

• FPKM: Useful for historical consistency but generally replaced by TPM.

• CPM: Ideal for differential expression analysis when gene length normalization is unnecessary.

Conclusion

Choosing the right normalization method depends on the specific objectives of your RNA-Seq analysis. TPM’s proportionality and robustness make it the preferred choice for most applications, while CPM serves well for differential expression studies. Although FPKM paved the way for RNA-Seq normalization, it has largely been supplanted by TPM in modern workflows. Understanding these methods and their nuances ensures accurate and meaningful interpretations of RNA-Seq data.

References:

1. Li, B., & Dewey, C. N. (2011). RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics.

2. Trapnell, C., et al. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology.

3. Law, C. W., et al. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology.

More at https://sites.temple.edu/liberles/

This idea, known as phylogenetic profiling, is the foundation of Profylo (https://github.com/MartinSchoenstein/Profylo), an open-source Python toolkit developed for comparing evolutionary profiles and discovering co-evolving genes. Profylo brings together seven profile-comparison methods and four approaches for identifying co-evolving gene clusters, making it easier to explore hidden functional relationships within genomes.

The study demonstrates that even a simple matrix of gene presence and absence can reveal meaningful biological patterns. By following these evolutionary footprints, researchers can generate new hypotheses about unknown genes, pathways, and molecular interactions. In a world where genomes are being sequenced faster than ever, tools like Profylo offer an exciting way to let evolution itself become a guide to understanding gene function.

More at https://link.springer.com/article/10.1007/s00239-025-10280-6 

To tackle this problem, researchers developed a new bioinformatics tool called ntSynt. It was first introduced in a 2025 study in BMC Biology titled “ntSynt: multi-genome synteny detection using minimizer graph mappings”. This tool (https://github.com/BirolLab/ntSynt) helps researcher find conserved genomic regions across several species without relying on traditional whole-genome alignments or gene annotations. Instead, ntSynt uses short DNA sequences called minimizers as landmarks. It finds minimizers shared by different genomes and maps their relationships in a graph, making it possible to spot large genomic regions that have kept a similar structure over time. The tool uses Bloom filters and graph-based mapping, which lets scientists analyze many genomes at once while saving memory and processing time.

The team tested ntSynt using both simulated data and real genomic sequences from groups like humans, chimpanzees, bonobos, gorillas, rodents, and bees. Their results showed that ntSynt can find conserved genomic regions and spot structural differences between species. In simulated tests, ntSynt identified syntenic blocks with over 97% accuracy. One standout example involved eleven bee genomes from the genus Andrena, which have a lot of variation in chromosome organization. Despite these differences, ntSynt found conserved regions covering about 85% of the smallest bee genome, completing the analysis in just 14.7 minutes and using less than 4 GB of memory. These findings highlight ntSynt’s potential for large-scale comparative genomics, where efficiency is crucial as more genomes become available.

An interesting result came up when the researchers compared primate genomes. When they looked at regions conserved between humans and other great apes, ntSynt flagged what looked like a large deletion in one gorilla genome assembly. After checking with long-read sequencing data, they found that the missing section was likely a mistake in the assembly, not a real evolutionary change. This means ntSynt can help not only with studying evolutionary relationships but also with spotting possible errors in genome assemblies. By highlighting unexpected breaks in regions that are usually conserved, the tool helps scientists tell apart true evolutionary changes from technical mistakes in reference sequences.

The importance of ntSynt becomes even clearer when we look at global projects to sequence and study Earth’s biodiversity. Projects like the Earth BioGenome Project aim to create genomic resources for many different organisms, opening up new ways to explore the history of life. But generating genomic data is just the beginning; making sense of these relationships requires powerful and scalable tools. ntSynt helps by letting researchers study chromosome evolution, conserved genome structures, and rearrangements across many species. While the tool has some limits, especially with detecting duplicated regions or analyzing very different genomes, its speed and scalability make it a valuable tool for modern comparative genomics.

The creation of ntSynt marks a significant advancement in our understanding of how genomes have evolved and become diverse over Earth's history. Since each conserved region in the genome is evidence of common ancestry and each rearrangement gives hinThe development of ntSynt is a big step forward in understanding how genomes have changed and diversified over time. Each conserved region in a genome points to shared ancestry, and each rearrangement offers clues about evolutionary events in different lineages. ntSynt helps scientists spot these patterns more easily, opening up new ways to study genome evolution across the tree of life. Evolution’s story isn’t just in the DNA sequence, but also in how those sequences are arranged and preserved over millions of years. Tools like ntSynt are helping scientists read this hidden story more clearly and reveal the deep genomic connections that link all life on Earth.

More at https://link.springer.com/article/10.1186/s12915-025-02455-w?

Address of the bookmark: http://gtpb.igc.gulbenkian.pt/bicourses/index.html

Address of the bookmark: http://grouthbio.com/Genome_Software_Service.php

Address of the bookmark: http://www.phyloxml.org/