Background: The Earth Biogenome Project has rapidly increased the number of available eukaryotic genomes, but most released genomes continue to lack annotation of protein-coding genes. In addition, no transcriptome data is available for some genomes. Results: Various gene annotation tools have been developed but each has its limitations. Here, we introduce GALBA, a fully automated pipeline that utilizes miniprot, a rapid protein-to-genome aligner, in combination with AUGUSTUS to predict genes with high accuracy. Accuracy results indicate that GALBA is particularly strong in the annotation of large vertebrate genomes. We also present use cases in insects, vertebrates, and a land plant. GALBA is fully open source and available as a docker image for easy execution with Singularity in high-performance computing environments. Conclusions: Our pipeline addresses the critical need for accurate gene annotation in newly sequenced genomes, and we believe that GALBA will greatly facilitate genome annotation for diverse organisms.

Sex differences in the size of specific brain structures have been extensively studied, but careful and reproducible statistical hypothesis testing to identify them produced overall small effect sizes and differences in brains of males and females. On the other hand, multivariate statistical or machine learning methods that analyze MR images of the whole brain have reported respectable accuracies for the task of distinguishing brains of males from brains of females. However, most existing studies lacked a careful control for brain volume differences between sexes and, if done, their accuracy often declined to 70% or below. This raises questions about the relevance of accuracies achieved without careful control of overall volume.

The Earth Biogenome Project has rapidly increased the number of available eukaryotic genomes, but most released genomes continue to lack annotation of protein-coding genes. In addition, no transcriptome data is available for some genomes. Various gene annotation tools have been developed but each has its limitations. Here, we introduce GALBA, a fully automated pipeline that utilizes miniprot, a rapid protein- to-genome aligner, in combination with AUGUSTUS to predict genes with high accuracy. Accuracy results indicate that GALBA is particularly strong in the annotation of large vertebrate genomes. We also present use cases in insects, vertebrates, and a previously unannotated land plant. GALBA is fully open source and available as a docker image for easy execution with Singularity in high-performance computing environments. Our pipeline addresses the critical need for accurate gene annotation in newly sequenced genomes, and we believe that GALBA will greatly facilitate genome annotation for diverse organisms.

Background: An important initial phase of arguably most homology search and alignment methods such as required for genome alignments is seed finding. The seed finding step is crucial to curb the runtime as potential alignments are restricted to and anchored at the sequence position pairs that constitute the seed. To identify seeds, it is good practice to use sets of spaced seed patterns, a method that locally compares two sequences and requires exact matches at certain positions only. Results: We introduce a new method for filtering alignment seeds that we call geometric hashing. Geometric hashing achieves a high specificity by combining non-local information from different seeds using a simple hash function that only requires a constant and small amount of additional time per spaced seed. Geometric hashing was tested on the task of finding homologous positions in the coding regions of human and mouse genome sequences. Thereby, the number of false positives was decreased about million-fold over sets of spaced seeds while maintaining a very high sensitivity. Conclusions: An additional geometric hashing filtering phase could improve the run-time, accuracy or both of programs for various homology-search-and-align tasks.

Background/Objectives: To evaluate whether automated three-dimensional volumetric body composition metrics from the Body and Organ Analysis (BOA) algorithm provide prognostic information beyond body mass index (BMI) for overall survival (OS) in surgically treated head and neck squamous cell carcinoma (HNSCC), and to compare cervical (C1–C7) measurements with thoracic and abdominal regional comparators. Methods: This single-center retrospective cohort study included 391 surgically treated HNSCC patients (2010–2019). Pretreatment CT scans were processed using the open-source BOA algorithm to obtain volumetric muscle, bone, and adipose measurements across cervical (C1–C7), thoracic, and abdominal regions. Composite indices—Sarcopenia Index (SI = Muscle/Bone) and Myosteatotic Fat Index (MFI = IMAT/TAT)—were derived; Cerv SI denotes the volumetric C1–C7 ratio. The predefined primary Cox model adjusted for age, sex, tumor site, UICC stage, treatment, HN-CCI, and secondary malignancy. Results: Of 391 patients (91% male, median follow-up 5.0 years, 141 deaths), BMI was not independently associated with OS (HR 0.88, p = 0.15). Cerv SI was associated with OS in the minimally adjusted model (HR 0.71, 95% CI 0.58–0.88, p = 0.002) and remained independent in the primary clinically adjusted model (HR 0.72, 95% CI 0.58–0.90, p = 0.004; C-index 0.67). When BMI and Cerv SI were entered together, Cerv SI remained independently associated (HR 0.72, p = 0.006) while BMI was null (HR 1.01, p = 0.95). Conclusions: BOA-derived cervical volumetric body composition is associated with overall survival beyond BMI and is obtainable from routine head and neck CT. Prospective validation and comparison with established two-dimensional C3/L3 methods are warranted before clinical use.

Background/Objectives: To evaluate whether automated three-dimensional (3D) volumetric body composition metrics derived from the Body and Organ Analysis (BOA) algorithm provide prognostic information beyond body mass index (BMI) for overall survival (OS) in patients with surgically treated head and neck squamous cell carcinoma (HNSCC), and to compare cervical (C1–C7) measurements with thoracic and abdominal benchmarks. Methods: This single-center retrospective cohort study included 391 surgically treated HNSCC patients at Universitätsmedizin Greifswald (2010–2019). Patients with definitive (chemo)radiation (no surgery) or non-standard adjuvant chemotherapy alone (n=13) were excluded; thus, all included patients had primary surgical management. Tumor staging was retrieved from institutional records for all 391 patients. For patients with documented ongoing follow-up but without a specific last-contact date, survival status was verified by chart review where possible, and patients confirmed alive at the institutional database snapshot date were administratively censored on 28 January 2025. Pretreatment CT scans were processed using the open-source BOA algorithm (v0.1.3) to obtain volumetric tissue measurements of muscle, bone, and adipose compartments across abdominal, thoracic, and cervical (C1–C7) regions. Composite indices — Sarcopenia Index (SI = Muscle/Bone) and Myosteatotic Fat Index (MFI = IMAT/TAT) — were derived. Univariate and multivariate Cox proportional hazards models were used. The primary clinically adjusted model included cervical SI, age, sex, tumor site, UICC stage, treatment modality, the Head and Neck Charlson Comorbidity Index (HN-CCI), and secondary malignancy; a minimally adjusted model (HN-CCI and secondary malignancy) was retained as a supportive analysis. Leukocytes and postoperative CRP were examined in a sensitivity model. Reporting followed the STROBE checklist. Results: Of 391 evaluable cases (91% male, median follow-up 5.0 years, 141 deaths), BMI showed only a weak univariate association with overall survival (HR 0.84, p=0.052) and was not independently associated with OS after adjustment (multivariate HR 0.88, p=0.15). In contrast, in the minimally adjusted supportive model the cervical Sarcopenia Index (Cerv SI, Muscle/Bone) was associated with overall survival (HR 0.71, 95% CI 0.58–0.88, p=0.002); in the primary clinically adjusted model including age, sex, tumor site, UICC stage, treatment modality, HN-CCI, and secondary malignancy, cervical SI remained independently associated with OS (HR 0.75, 95% CI 0.61–0.92, p=0.007; n=254). Thoracic Myosteatotic Fat Index (Thor MFI) was associated with worse overall survival in the minimally adjusted supportive model (HR 1.35, p=0.002) and remained associated after extended oncologic adjustment (HR 1.26, p=0.013). Harrell's concordance index for overall survival prediction was 0.60 for cervical SI and 0.55 for BMI; the difference was modest. Inter-regional SI correlations were strong between abdomen and thorax (r=0.69) but only moderate for cervical–thoracic (r=0.42). An in-sample comparison showed that a BOA-derived single-level C3 Sarcopenia Index was non-prognostic (C-index 0.52) and weakly correlated with the volumetric C1–C7 SI (r=0.04), suggesting that the volumetric and single-vertebra-level metrics derived from the BOA pipeline capture different information. In paired bootstrap comparisons, cervical SI showed a modest but significant C-index improvement over BMI (ΔC = +0.087, p = 0.038) but did not significantly outperform the BOA-derived C3 SI, ASA classification, or thoracic SI. Conclusions: BOA-derived volumetric body composition indices, particularly the cervical Sarcopenia Index, are associated with overall survival in surgically treated HNSCC patients and remain prognostic after adjustment for established oncologic confounders, whereas BMI is not. Cervical C1–C7 BOA analysis enables body composition assessment from routine head and neck CT and may provide an objective imaging-derived marker for future risk-stratification studies. Prospective validation and formal comparison with established C3/L3 methods are warranted before routine clinical use.