Combining DNA and protein alignments to improve genome annotation with LiftOn

A two-step PM algorithm to improve protein-coding gene annotations

LiftOn implements a two-step PM algorithm (illustrated in Fig. 1 and discussed in more detail in Methods) to find the best annotations at protein-coding gene loci. First, it uses a chaining algorithm, described below, to find the exon–intron boundaries of protein-coding transcripts. Second, if the CDS does not preserve the entire protein sequence of the reference protein, it adjusts the CDS boundaries to preserve as much of the reference protein as possible (note that CDS features are defined as the coding portions of exons, from start to stop, and excluding untranslated portions of exons).

Figure 1.

The protein-maximization (PM) algorithm consists of two modules: (A–E) the chaining algorithm and (F–K) the open reading frame (ORF) search algorithm. (A) Matched protein-coding transcripts mapped by Liftoff (green) and miniprot (orange) at the same location in a target genome. The transcript in blue represents the correct transcript annotation on the target genome. Liftoff's mapping has an erroneous splice junction between L3 and L4, whereas miniprot's mapping has a missing splice junction in M6. (B) Pairwise alignment results of the proteins mapped by Liftoff and miniprot to the reference protein. The figure shows a premature stop codon in the Liftoff protein, and the miniprot alignment has a mismatched protein sequence at the end. (C) Pairwise alignment mappings with added exon/CDS boundaries. (D) CDSs are grouped based on the cumulative lengths of the amino acids in the reference protein as described in the main text. In this example, CDSs are organized into groups: $G_{L_1}=\left\{L1\right\}$, $G_{M_1}=\left\{M1\right\}$, $G_{L_2}=\left\{L2\right\}$, $G_{M_2}=\left\{M2\right\}$, $G_{L_3}=\left\{L3,L4\right\}$, $G_{M_3}=\left\{M3,M4\right\}$, $G_{L_4}=\left\{L5\right\}$, $G_{M_4}=\left\{M5\right\}$, $G_{L_5}=\left\{L6,L7\right\}$, and $G_{M_5}=\left\{M6\right\}$. The chaining algorithm iterates through each group, comparing the corresponding partial protein sequences to the reference protein and chaining those with higher protein sequence identity. (E) In this example, $G_{L_1}$, $G_{L_2}$, $G_{M_3}$, $G_{L_4}$, and $G_{L_5}$ are chained, forming the new protein-coding transcript CDS list. This list includes L1, L2, M3, M4, L5, L6, and L7 in the LiftOn annotation. (F–K) Schematic diagrams illustrating how the ORF search algorithm handles various types of sequence mutations. This process leads to changes in the gene annotation of both translated and untranslated regions (UTRs). (F) A frameshift mutation is a variation caused by the insertion or deletion of a sequence of nucleotides whose length is not divisible by three. In this example, the indel introduces a premature stop codon. (G,H) Point mutations leading to premature stop codons. LiftOn searches for the longest ORF, considering two scenarios: G depicts the selection of the first encountered stop codon, and H illustrates the switch to the downstream start codon. (I) Stop codon loss. When a stop codon is deleted, LiftOn identifies a new stop codon in the 3′ UTR. (J,K) Start codon loss. In this scenario, LiftOn searches for a new start codon, exploring both downstream in the coding region (J) and upstream in the 5′ UTR (K).

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The chaining algorithm (Fig. 1A–E) starts by pairing up miniprot (Li 2023) alignments with transcripts identified by Liftoff (for details on the pairing approach, see Methods and Algorithm S1). After two transcripts are paired, the protein sequences from the Liftoff and miniprot annotations are then aligned to the full-length reference protein, as illustrated in Figure 1B. Subsequently, LiftOn maps the CDS boundaries from both the Liftoff and miniprot annotations onto the protein alignment (Fig. 1C; Algorithm S2).

The CDSs within the Liftoff and miniprot annotations are grouped in the 5′ to 3′ direction. The CDS groups are represented as $G_{L_i}$ and $G_{M_i}$, respectively, for LiftOff and miniprot, where i denotes the ith group in that annotation (Fig. 1D).

The grouping process begins with the first CDS in each annotation and continues until reaching the endpoints of the downstream CDS in Liftoff and miniprot, where the number of aligned amino acids from the reference protein is equal. This forms the first CDS group in Liftoff, denoted as $G_{L_1}$, and the first CDS group in miniprot, denoted as $G_{M_1}$. Subsequent groups start from the previous endpoint in both Liftoff and miniprot, extending until the number of aligned amino acids from the reference protein matches for both annotations again. These subsequent groups are represented as $G_{L_2}$ and $G_{M_2}$, respectively. The grouping process concludes upon reaching the last CDS in both annotations (for more details, see Algorithm S3).

Within each group, $G_{L_i}$ or $G_{M_i}$, we calculate the partial protein sequence identity and select the group with higher protein sequence identity score (Fig. 1D; Algorithm S4). In case of a tie, LiftOn prioritizes the Liftoff annotation, $G_{L_i}$, so that it will include UTRs in its output. The selected group of CDSs is represented by $G_{SE{L}_i}$. All CDSs in $G_{SE{L}_i}$ are then concatenated to form the final LiftOn transcript, as shown in Figure 1E. This transcript is an ordered sequence of CDSs sourced from either Liftoff or miniprot, with the goal of maximizing protein similarity to the reference protein. This approach is particularly effective in addressing issues such as in-frame indels or mis-splicing that may arise from misalignments, as illustrated in Figure 1A.

After applying the chaining algorithm, LiftOn attempts to make further improvements in the CDS regions, as illustrated in Figure 1, F–K. It searches the translations of protein-coding transcripts and adjusts CDS boundaries to avoid early stop codons (Fig. 1F,G), choose better translation start sites (Fig. 1H,J,K), or extend proteins with stop codon loss (Fig. 1I). Supplemental Figure S2 provides additional Integrative Genomics Viewer (IGV) (Robinson et al. 2011; Thorvaldsdóttir et al. 2013) screenshots that illustrate LiftOn's results for each scenario. After making these adjustments, LiftOn evaluates the differences between the reference and target transcripts and, similarly to LiftoffTools (Shumate and Salzberg 2022), produces a mutation report. Transcripts are deemed “identical” when their target and reference gene DNA sequences are entirely the same. For nonidentical sequences, LiftOn categorizes their differences using these categories: synonymous, nonsynonymous, in-frame insertion, in-frame deletion, frameshift, stop codon gain, stop codon loss, and start codon loss.

Lifton improves human annotation lift-over

For our first experiment, we used human annotation from RefSeq release 220 (GCF_000001405.40-RS_2023_03) (Pruitt et al. 2007) as the reference annotation to map annotations from the GRCh38 onto the T2T-CHM13 (Supplemental Fig. S3). In total, the reference annotation contained 37,986 protein-coding and noncoding genes and 160,561 transcripts, of which 130,528 were protein-coding (for all mapped genes, see Supplemental Table S1; for our gene counting approach, see Methods). We focused our analysis on the protein-coding transcripts because those are the ones for which LiftOn can produce improvements over DNA-based methods. At the gene level, LiftOn successfully lifted over 37,828 genes, whereas 158 genes remained unmapped, of which 103 were protein-coding (Supplemental Table S2). The overall gene mapping rate was 99.6%. All 158 genes mapped to the locations of other genes on the target genome, likely owing to copy number variation. Out of the successfully mapped genes, 37,453 (19,738 protein-coding and 17,715 noncoding) were mapped as single copies, and 375 genes (86 protein-coding and 289 noncoding) were mapped with extra copies. In total, 38,886 gene loci were mapped onto T2T-CHM13 (Table 1).

In assessing overall protein-coding gene annotations, we computed gap-compressed protein sequence identity scores for LiftOn, Liftoff, and miniprot. Figure 2A compares the mapping of protein-coding genes using Liftoff, which is based on DNA alignment, to miniprot, which uses protein-to-DNA alignment. Dots in the lower right part of the panel indicate transcripts for which Liftoff was superior to miniprot, as measured by protein sequence identity, and the upper left part of the plot shows transcripts for which miniprot was superior. This comparison shows that neither approach dominates the other. In the LiftOn versus Liftoff comparison (Fig. 2B), 866 transcripts exhibit higher protein sequence identity, with 113 of those achieving 100% identity. Similarly, the LiftOn versus miniprot comparison (Fig. 2C) shows that LiftOn finds better matches for 30,266 protein-coding transcripts, improving 22,746 to 100% identity. The protein sequence identity distributions are shown in Supplemental Figure S4.

Figure 2.

Comparative analysis of various tools for mapping RefSeq protein annotations from GRCh38 to T2T-CHM13 v2.0. (A–C) Scatter plots of protein sequence identity. (A) Comparison between miniprot (y-axis) and Liftoff (x-axis), (B) comparison between LiftOn (y-axis) and Liftoff (x-axis), and (C) comparison between LiftOn (y-axis) and miniprot (x-axis). (D–G) Examples of improved annotation owing to LiftOn's two-step PM algorithm. (D) LiftOn uses Liftoff's annotation to correct a splice junction missed by miniprot in transcript NM_001083965.2 of the TDRKH gene. (E) For transcript NM_001384763.1 of the SLC22A31 gene, LiftOn uses miniprot's annotation to resolve an incorrect acceptor site from Liftoff's annotation. (F) For transcript XM_011517662.4 of the WASHC1 gene, LiftOn combines both annotations to rectify an omitted CDS by miniprot and a misidentified splice junction by Liftoff between the fifth and sixth exons. (G) LiftOn's ORF search algorithm selects an alternative downstream start codon for a frameshift mutation in transcript NM_001004692.2 of the OR2T12 gene, thereby conserving the majority of the protein sequence.

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The LiftOn PM algorithm can improve protein-coding transcript annotations in the four scenarios illustrated in Figure 2. First, as observed with the TDRKH gene (Fig. 2D; Supplemental Fig. S5A), miniprot missed a splice junction, leading to the retention of an intron and a premature stop codon. Here LiftOn adopted Liftoff's CDS, which yielded the correct protein. Second, as shown in Figure 2E and Supplemental Figure S5B for the SLC22A31 gene, miniprot correctly identified an AG acceptor site that Liftoff missed, yielding the correct CDS, which LiftOn combined with the UTRs from Liftoff. Third, LiftOn sometimes was able to fix errors in both methods, as shown in Figure 2F and Supplemental Figure S5C for the gene WASHC1. In this example, LiftOn corrected the omission of the first coding exon by miniprot and fixed an incorrect splice junction found by Liftoff, one that erroneously introduced a premature stop codon between the fifth and sixth exons. LiftOn's chaining algorithm effectively consolidates these segments, improving the protein sequence identity to 99.4%. Fourth, as shown in Figure 2G for the OR2T12 gene, a mutation introduced a premature stop codon (Fig. 2G, inset), and the ORF search algorithm in LiftOn adjusted by scanning for ORFs that best match the reference protein sequence. Here, it found an alternative start codon just two codons downstream that maintained a near full-length match to the reference protein.

LiftOn identifies and reports additional copies of lifted-over features, similarly to Liftoff. In the mapping from GRCh38 to CHM13, LiftOn identified 86 protein-coding genes with at least one extra copy, for a total of 320 new protein-coding gene loci. Of these, 289 extra gene copies were detected by Liftoff and 31 by miniprot, detailed in Supplemental Tables S3 and S4. The Circos plot (Krzywinski et al. 2009) shows the relative positions of extra gene copies between the two genomes, illustrating that most were located on the same chromosomes (Fig. 3).

Figure 3.

Plot illustrating the locations of extra gene copies found on T2T-CHM13 (left side) compared to GRCh38 (right side). The Circos plot was generated using pyCircos (https://github.com/ponnhide/pyCircos). Each line represents a gene copy mapped from the reference genome to the target genome, with colors indicating different chromosomes. The lines are color-coded by the chromosome of the original copy. The bands within the plot are sized proportionally to reflect the actual size of these chromosomes.

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We further compared the LiftOn-generated annotation of CHM13 with the current release of the T2T-CHM13 annotation (available from the T2T GitHub site, https://github.com/marbl/CHM13; https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/annotation/chm13v2.0_RefSeq_Liftoff_v5.2.gff3.gz). We excluded the annotation of Chromosome Y from this analysis owing to the extremely complex repeat structure of that chromosome, which confounds most attempts to align genes consistently to it (Rhie et al. 2023). Our comparison considered only protein-coding transcripts on Chromosomes 1–22 and X. LiftOn's annotation contained 665 protein-coding transcripts that had a higher protein sequence identity to the corresponding proteins on GRCh38, compared with the current CHM13 annotation. Four examples are shown in Figure 4A–D, each of which illustrates how LiftOn can improve the fidelity of the match between the source protein and the mapped-over version.

Figure 4.

Examples in which LiftOn improves the current T2T-CHM13 annotation. (A) In the NM_006065.5 transcript of the SIRPB1 gene, the current CHM13 annotation omits three coding exons. The LiftOn version finds those exons and increases the DNA sequence identity from 81% to 98%. (B) In transcript NM_001134939.1 of OAZ3, the CHM13 annotation incorporates a partial CDS in the second exon, leading to a truncated protein. LiftOn corrects this misannotation, increasing the protein sequence identity from 5.42% to 100%. (C) In transcript XM_047448259.1 of EPHA2, the published annotation chooses the wrong start codon. LiftOn finds a better start codon that improves the protein sequence identity from 2.4% to 98.7%. (D) In transcript NM_001099772.2 from CYP4B1, LiftOn shifts the donor site of the seventh coding exon by 11 nucleotides, fixing a frameshift and improving the protein sequence identity from 53% to 99%.

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Among the 130,528 protein-coding transcripts mapped onto CHM13 by LiftOn, 13,486 transcripts were identical and 86,335 had only synonymous mutations. Of the remaining 30,707 transcripts, only 1046 had a protein with <95% identity to the reference protein. Overall, LiftOn's mapped-over annotation has greater fidelity to the source annotation (from GRCh38) than the current T2T-CHM13 annotation (Supplemental Fig. S6).

We observed similar improvements (compared with both Liftoff and miniprot) when we lifted over different human annotations, including MANE (Supplemental Note S1; Supplemental Table S5; Supplemental Fig. S7; Morales et al. 2022) and CHESS3 (Supplemental Note S2; Supplemental Table S6; Supplemental Fig. S8; Varabyou et al. 2023). Additionally, to assess the ability of LiftOn to map annotation between genomes of non-human species, we ran experiments on two different genomes from each of four plant and animal species—M. musculus (Supplemental Note S3; Supplemental Table S7; Supplemental Fig. S9; Chinwalla et al. 2002; Schmid-Siegert et al. 2023), A. mellifera (honeybee) (see Supplemental Note S4; Supplemental Table S8; Supplemental Fig. S10; Wallberg et al. 2019; Cao et al. 2021), O. sativa (Asian rice) (Supplemental Note S5; Supplemental Table S9; Supplemental Fig. S11; Kawahara et al. 2013; Shang et al. 2023), and A. thaliana (Supplemental Note S6; Supplemental Table S10; Supplemental Fig. S12; Lamesch et al. 2012; Naish et al. 2021)—and obtained comparable enhancements. These analyses demonstrate the robust performance of LiftOn in handling annotation lift-over across a broad spectrum of organisms.

LiftOn improves both closely and distantly related species annotation lift-over

To demonstrate that LiftOn can reliably lift-over cross-species annotations, we used it to transfer genes between both closely related and more distantly related species. We measured genomic distance using Dashing 2 (Baker and Langmead 2023) and Mash (Ondov et al. 2016).

Mapping from Homo sapiens to P. troglodytes

Chimpanzees are the closest evolutionary relatives of humans (Goodman 1999; Ebersberger et al. 2002; Chimpanzee Sequencing and Analysis Consortium 2005; Prüfer et al. 2012), so we chose this example to demonstrate the applicability of LiftOn between closely related species. LiftOn successfully lifted-over 37,509 genes (19,485 + 157 + 17,596 + 271) (Table 2), leaving 477 (285 + 192) genes unmapped, of which 285 were protein-coding and 192 noncoding. The overall gene mapping rate was 98.7%. Out of all mapped genes, 37,081 (19,485 + 17,596) were mapped uniquely, whereas 428 (157 + 271) genes were mapped to multiple locations, producing a total of 38,879 gene loci. LiftOn obtained higher protein sequence identity scores than Liftoff and miniprot for 4332 and 33,509 transcripts, respectively (Fig. 5A; Supplemental Note S7; Supplemental Fig. S13). We also compared LiftOn, Liftoff, and miniprot annotations to the published chimpanzee annotation, GCF_028858775.1_NHGRI_mPanTro3-v1.1-hic.freeze_pri, and found that LiftOn's results were superior to both Liftoff and miniprot as measured by matching intron chains and transcripts (Supplemental Table S11).

Figure 5.

Evaluation of gene annotation lifting across species and tools using gene order plots, protein identity dot plots, and frequency distributions. (A) Comparative analysis of lifting over RefSeq v220 annotations from Homo sapiens (GRCh38) to Pan troglodytes (NHGRI_mPanTro3-v1.1). (B) Comparative analysis of lifting over annotations from Drosophila melanogaster (genome assembly release 6 + ISO1MT) to Drosophila erecta (Prin_Dsim_3.1). (C) Comparative analysis of lifting over annotations from Mus musculus (GRCm39) to Rattus norvegicus (mRatBN7.2). Graphs labeled a show protein-gene order plots, with the x-axis representing the reference genome and the y-axis representing the target genome. The protein sequence identities are color-coded on a logarithmic scale, ranging from green (one) to red (zero), and represent the degree of amino acid similarity, with one indicating identical sequences and zero indicating no shared amino acids. The gene order plot script was customized from LiftoffTools. Graphs labeled b are 3D protein sequence identity plots comparing Liftoff on the x-axis, miniprot on the y-axis, and LiftOn on the z-axis. Each dot represents a protein-coding transcript. If a dot is above the x = y plane, LiftOn's mapping produced a higher protein sequence identity score than the other programs. Graphs labeled c are frequency plots on a logarithmic scale of protein sequence identity for LiftOn (left), Liftoff (middle), and miniprot (right).

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Mapping from D. melanogaster to D. erecta

We then mapped the annotation between two Drosophila species (Adams et al. 2000; Clark et al. 2007) that are considerably more distant from each other than human and chimpanzee—D. melanogaster (Hoskins et al. 2015) and D. erecta (Rio et al. 1983; David et al. 2007)—with 0.07 Dashing 2 (Baker and Langmead 2023) similarity score and 0.08 Mash-distance (Ondov et al. 2016). A protein-coding gene order plot (Fig. 5B) shows multiple genome rearrangements, illustrating the divergence between the genomes. The LiftOn gene- and transcript-level statistics are summarized in Table 2, showing a gene mapping rate of 94.5% and a transcript mapping rate of 96.4%. LiftOn yields improved annotations for 4583 and 7763 protein-coding transcripts compared with Liftoff and miniprot, respectively (Supplemental Note S8; Supplemental Fig. S14).

Mapping from M. musculus to R. norvegicus

Finally, we chose two model organisms, M. musculus (Church et al. 2011) and R. norvegicus (Howe et al. 2021) (mouse and rat), to showcase LiftOn's ability to map annotation between even more distant species, with 0.01 Dashing 2 (Baker and Langmead 2023) similarity score and 0.12 Mash-distance (Ondov et al. 2016). The results are illustrated in Figure 5C and Table 2, which reveal a gene mapping rate of 94.3% and a transcript mapping rate of 96.6%. For the mouse-to-rat mapping, LiftOn improves the annotations for 15,420 and 30,574 protein-coding transcripts compared with Liftoff and miniprot, respectively. The frequency plots in Figure 5C reveal that LiftOn only had 2492 protein-coding transcripts below the 40% identity threshold, a notable improvement over Liftoff (9574) and miniprot (9292). More details are included in Supplemental Note S9 and Supplemental Figures S15 and S16. We also compared LiftOn, Liftoff, and miniprot annotations to the published mRatBN7.2 rat annotation and found that LiftOn was superior in terms of matching intron chains and transcripts (Supplemental Table S12).

Annotation lift-over special handling of the human reference

For all mappings of the human annotation, we excluded all alternative scaffolds and patches from the GRCh38 genome and its annotation. Specifically, we excluded scaffolds ending in “_fix” and “_alt,” because they are duplicates or variants of sequences found on the primary chromosomes. We also excluded rRNAs, which occur in hundreds of identical copies that vary widely among humans and create problems for the alignment programs (Agrawal and Ganley 2018; Hori et al. 2021; Chao et al. 2023).

Pairing Liftoff and miniprot protein-coding transcript annotations

Liftoff was designed to map any genomic interval, containing genes, transcripts, and exons, from one genome onto another. Liftoff's output uses a hierarchy in which genes contain one or more transcripts, and transcripts contain exons and/or CDS features. A CDS feature defines the CDSs within a protein-coding transcript; therefore, some CDS features span the same intervals as exons, whereas others span only parts of exons when the exons also include UTRs. In contrast, miniprot was designed to map protein sequences to a genome and can only generate annotations of transcripts containing CDS features.

After running both Liftoff and miniprot, LiftOn goes through the miniprot mappings and finds, for each one, the Liftoff genomic interval to which it corresponds. A miniprot transcript is considered to match a Liftoff transcript if (1) the loci of the two transcripts overlap, and (2) the locus of the miniprot transcript does not overlap with any other Liftoff gene loci, except those for which the matched Liftoff locus also overlaps. Note that the genomic intervals found by Liftoff are mostly distinct, but it does allow up to 10% overlap between adjacent gene features by default. On the other hand, miniprot lacks the capability to reconcile overlapping gene loci, which sometimes results in a protein-coding transcript from a large gene family mapping to multiple gene loci instead of one or to both a pseudogene and the correct locus.

In most cases, LiftOn is able to find a one-to-one mapping between the miniprot transcripts and those from LiftOff. For instance, mapping RefSeq release 220 annotations from GRCh38.p14 to T2T-CHM13 v2 results in 128,351 of 136,978 protein-coding transcripts (93.7%) being uniquely matched between Liftoff and miniprot (see Supplemental Note S10; Supplemental Fig. S17).

For the cases in which miniprot identifies multiple copies for a protein-coding transcript, LiftOn checks whether there is at least one copy overlapping with a Liftoff gene locus. Miniprot transcript mappings spanning multiple Liftoff loci are removed to prevent erroneous “read through” annotations. Among the remaining options, the transcript with the highest protein sequence identity score is selected. Note that in the GRCh38.p14 to T2T-CHM13 v2 lift-over there were 355 protein-coding transcripts in which none of the miniprot-discovered protein-coding transcripts overlapped with a corresponding Liftoff gene locus.

Once the one-to-one correspondence between two transcripts is established, LiftOn considers both Liftoff and miniprot CDS features of the two transcripts and initiates its PM algorithm as described in the next Methods section.

PM algorithm

The PM algorithm consists of two components: the chaining algorithm and the ORF search algorithm. The pseudocode for the protein_maximization function is outlined in Algorithm S1.

DNA and protein transcript sequence identity score calculation

To compute DNA sequence identity scores, LiftOn first extracts transcript sequences by concatenating all exonic regions in a transcript. Subsequently, it aligns each transcript sequence mapped on the target genome by LiftOn, Liftoff, or miniprot to its respective reference sequence. This alignment is performed using the nw_trace_scan_sat function from the Parasail (Daily 2016) Python package, configured with a match score of one, mismatch penalty of −3, gap opening penalty of two, and gap extension penalty of two. LiftOn then reports the percentage of identity between the two aligned sequences, defined as in BLAST (Altschul et al. 1990) as the number of matching bases in the two sequences over the number of alignment columns. The pseudocode of the algorithm described in this paragraph is illustrated in the get_DNA_id_fraction function from Algorithm S4. An example is provided in Supplemental Figure S18A.

To compute protein sequence identity scores, LiftOn first generates the protein sequence for each mapped transcript by translating the sequence obtained from concatenating all CDS regions in the transcript. Then, it aligns each protein sequence with the full-length reference protein by performing a pairwise alignment using the BLOSUM 62 matrix (Henikoff and Henikoff 1992), a gap opening penalty of 11 for insertions and deletions (indels), and a gap extension penalty of two. The protein sequence identity score is calculated up to the first encountered stop codon in the target protein. Differing slightly from the BLAST-style metric used for DNA sequence identity, LiftOn compresses the gaps in the reference alignment to prevent overpenalization of longer proteins in the target genome. These proteins might be mapped as longer because of potential repeat regions within the target genome or because of potential truncations in the proteins of the reference genome. The pseudocode for this process is presented in the get_AA_id_fraction function in Algorithm S4. An example is provided in Supplemental Figure S18B.

Additionally, the get_partial_id_fraction function in Algorithm S4 describes this process when the sequence identity computation is restricted to evaluating matches between partial proteins, as in the chaining algorithm, to determine the best matching CDS group with respect to the reference.

Resolving overlapping gene loci and finding extra copies of protein-coding genes

LiftOn uses both Liftoff and miniprot to identify additional copies of protein-coding genes, prioritizing Liftoff for its capability to also map UTR regions. First, LiftOn uses the embedded Liftoff module to iteratively map new gene copies and verify whether the lifted-over gene loci overlap with existing annotations. If so, it allows overlaps that are also present in the reference genome; if not, it permits a maximum overlap of 10% with other gene loci. The Liftoff module also ensures that each gene copy meets the user-specified minimum DNA sequence identity between the reference and target gene loci. This process continues until all possible copies have been identified.

Then, LiftOn applies the miniprot module to identify any additional copies of protein-coding genes that Liftoff might have missed. LiftOn uses the intervaltree package, a self-balancing interval tree implemented in Python (https://github.com/chaimleib/intervaltree), to manage gene loci intervals and enable fast detection of overlaps. Because miniprot maps annotations at the protein-coding transcript (mRNA) level, LiftOn copies the corresponding gene-level feature from the reference annotation as the parent of the mRNA feature identified exclusively by miniprot to ensure consistency within the “gene–mRNA–exon” hierarchy. LiftOn also enforces the constraint that any extra gene copies identified by miniprot should have at most a 10% overlap with other gene loci (computed as the ratio of the mapped miniprot length to the smaller of the two values: the mapped miniprot length or the reference protein length).

Furthermore, because miniprot maps only proteins and does not consider the UTRs in gene loci, it is very likely that miniprot maps to a processed pseudogene or only maps a partial gene segment. To remove potential pseudogenes, we implemented stricter filtering rules for gene loci identified exclusively by miniprot. First, if miniprot identifies only one CDS (i.e., with no intervening introns), the gene locus is annotated only if there is also a single CDS in the reference annotation. Second, the ratio of the coding regions in the miniprot annotation to the coding regions in the longest isoform of a reference gene locus must be between 0.9 and 1.5. Note that any extra gene copies identified exclusively by miniprot will not include UTRs.

LiftOn arguments used in the study

All analyses in this study were performed using LiftOn with its default parameters along with the additional arguments “-copies,” “-sc 0.95,” and “-polish.” The “-copies” argument prompts Liftoff to search for extra gene copies following the initial lift-over process. A gene copy will be annotated at a specific locus only if there is no overlap with an already annotated feature. Accompanying the “-copies” argument, the “-sc 0.95” parameter modifies the default Liftoff requirement of 100% sequence identity of mapped exons/CDSs to the reference ones, reducing it to allow a 95% sequence identity. The “-copies” argument will also trigger the annotation of extra copies of gene loci, which were distinctly found by miniport.

The “-polish” argument enables the Liftoff step in LiftOn to realign exons to correct CDSs that may have been altered during the lift-over process, such as the loss of start/stop codons or the introduction of in-frame stop codons, although this step extends the processing time.

To map RefSeq release v220 (results 2.2), MANE version 1.2 (Supplemental Note S1; Supplemental Table S4; Supplemental Fig. S7), and CHESS 3 (Supplemental Note S2; Supplemental Table S5; Supplemental Fig. S8) human annotations from GRCh38 to CHM13 (Table 1; Fig. 2), the “-chroms<chromosome_mapping.txt>” argument was also used in order to first perform a mapping step on a chromosome-by-chromosome basis. Once this step is complete, any genes that remain unmapped are remapped to the entire target assembly. This setting resulted in improved human annotation mapping accuracy. For all other experiments, LiftOn was run without the “-chroms” argument, mapping gene sequences to the full genome.

The “-f” argument and the input file “features.txt” indicate all types of parent features to lift over. To ensure LiftOn does not mistakenly identify pseudogenes as extra gene copies, we ran LiftOn with the “features.txt” file specifying both “gene” and “pseudogene” features to lift them over along with their children.

LiftOn runs miniprot with default parameters plus the “-gff-only” option to exclusively generate a GFF output file. Users can customize miniprot arguments to suit their specific data needs using the “-mp_options” parameter. This parameter should be followed by a string of options, separated by an “=” sign. The “=” is crucial, as it distinguishes miniprot options from Liftoff options that share the same flag. We recommend using the “-mp_options=“-j 2”” for splicing alignment in mammals and “-mp_options=“-j 1”” for nonmammals.

All experiments were conducted on a 24-core, 48-thread Intel Xeon Gold 6248R Linux computer with 1024 GB memory, using a single thread of execution.

Gene and transcript feature counting

The counts of protein-coding and noncoding genes and transcripts reported in this study were calculated as follows.

Running LiftOn on a test data set

We provide sample files for the users wanting to test LiftOn at our GitHub repository: https://github.com/Kuanhao-Chao/LiftOn/tree/main/test. To execute LiftOn, the user can type a single command that uses the “-g” flag to specify a reference annotation file in GFF format (e.g., “NCBI_RefSeq_no_rRNA.gff”), the “-o” flag to specify the output file (e.g., “lifton.gff3”), plus both the reference genome file (e.g., “GCF_000001405.40_GRCh38.p14_genomic.fna”) and the target genome file (e.g., “chm13v2.0.fa”). For example, lifton -g NCBI_RefSeq_no_rRNA.gff -o lifton.gff3 -copies chm13v2.0.fa GCF_000001405.40_GRCh38.p14_genomic.fna.

Genomes used in this study

Brief rationales for selecting the genomes used in our experiments are described below. NCBI GenBank (https://www.ncbi.nlm.nih.gov/genbank/) accession numbers, assembly names, URLs for genome assembly and annotation downloads, and annotation release or last updated dates are summarized in Table 4.

Software availability

LiftOn is implemented as a Python package. The LiftOn project is freely available on GitHub (https://github.com/Kuanhao-Chao/LiftOn) and on PyPi (https://pypi.org/project/LiftOn/).

The LiftOn source code can also be found as Supplemental Code. The LiftOn documentation is available at https://ccb.jhu.edu/lifton/.